reimbursements for dtcs in the netherlands - supporting their accuracy in ocular care

Reimbursements for Diagnosis Treatment

Combinations in the Netherlands

Supporting their Accuracy in Ocular Care

Master Thesis

by

Gunnar Magnús Ballzus

Medical Informatics

November 2012

Academic Medical Center, University of Amsterdam


Combinations in the Netherlands


Master Thesis

by


The cover page picture illustrates Landolt C in 4 positions, a standardized symbol used for measuring visual performance.


Combinations in Ocular Care

Supporting their Accuracy

Student: Gunnar Magnús Ballzus, BSc Student number: 6194907 [email protected]

SRP address: Academic Medical Center Dept. of Medical Informatics Meibergdreef 9 1105 AZ Amsterdam

Mentor: H. Stevie Tan, PhD Dept. of Ophthalmology AMC-UvA [email protected]

Tutors: Nicolette de Keizer, PhD Dept of Medical Informatics AMC-UvA [email protected] Ronald Cornet, PhD Dept of Medical Informatics AMC-UvA [email protected]

Period of Scientific Research Project November 2011 – November 2012

Acknowledgments This thesis is the result of my Scientific Research Project, the last step towards finishing my

Master's degree in Medical Informatics. I am grateful to all those who helped me in making

the successful completion of the thesis possible. However, I want to specify some of these

people in particular.

Nicolet and Ronald, I owe you many thanks for your guidance throughout the project. I

could not have wished for better tutors. Your positive attitudes and willingness to assist me

was inspiring. Thank you for helping me to clarify my thoughts and stay focused.

Furthermore, thank you for your contribution to writing the thesis.

Stevie, it is simple enough to say that without you, this project would not have been

realized. Despite the limited time you have, you took the time to help me get a grasp of how

the DBC works and how Ocular Care takes place. Moreover, you participated in the

requirement elicitation and provided me with data for the comparative study, half of which

you collected yourself. Thank you for all this and the collaboration.

Linda, some people are always a breeze to work with, and you are one of them. Thank

you for taking the time to help me in conducting the requirement elicitation during the

study. I owe you one, do not forget that.

Hákon and Hannah, my friends across the Atlantic Ocean: It is a privilege to have friends

that are always there for you despite living far away. Thank you for taking the time to read

over my thesis. Your feedback unquestionably improved the thesis.

Dear Mom and Dad, I am ever grateful to you for making it possible for me to relocate to

the Netherlands and return to school. Thank you for your support and always being there

for me.

At last Nynke, my love, being with you makes me happy every single day. Your support

is invaluable to me. Thank you for all the good times together and the many that will follow.


Nieuwegein, November 2012

Contents SUMMARY IV

SAMENVATTING V

ABBREVIATIONS AND TRANSLATIONS VII

CHAPTER 1 1

1.1. CONTEXT 2

1.2. RESEARCH QUESTIONS 3

1.3. OUTLINE OF THE THESIS 4

CHAPTER 2 5

2.1. DEFINITION OF OCULAR CARE 6

2.2. VISUAL IMPAIRMENTS AND OCULAR CARE 6

2.2.1. VISUAL IMPAIRMENTS AND OCULAR CARE IN THE NETHERLANDS 6

2.2.2. OCULAR CARE AT THE AMC 6

2.3. DOT REIMBURSEMENTS IN OCULAR CARE 8

2.3.1. RECORD 9

2.3.2. EXTRACT 12

2.3.3. DEDUCE 12

2.3.4. INVOICE 13

CHAPTER 3 15

3.1. SUPPORT ACCURATE DOT REIMBURSEMENTS IN OCULAR CARE 16

3.1.1. REQUIREMENTS ELICITATION USING APPLIED COGNITIVE TASK ANALYSIS 16

3.1.2. DESIGNING FUNCTIONAL REQUIREMENTS 17

3.2. COMPARISON OF DOT REIMBURSEMENTS IN OCULAR CARE 18

3.2.1. SUBJECTS 19

3.2.2. PROTOCOL DESIGN 19

3.2.3. OUTCOME MEASURES AND DATA ANALYSIS 21

3.2.4. ETHICAL CONSIDERATIONS 22

CHAPTER 4 23



4.2.1. DOT REIMBURSEMENTS 28

4.2.2. UNDERLYING DTCS OF THE DOT REIMBURSEMENTS 29

iii Contents

CHAPTER 5 31

5.1. MAIN FINDINGS 32

5.2. TAKEAWAY MESSAGE FROM THIS STUDY 32

5.3. LIMITATIONS OF THE STUDY 33

5.4. THE RESULTS IN RELATION TO OTHER STUDIES 34

5.5. IMPLICATIONS 35

5.6. FUTURE RESEARCH 35

REFERENCES 37

APPENDICES 43

APPENDIX A : EXEMPTION RULES FOR CLOSING OCULAR EC SEGMENT 44

APPENDIX B : DIAGNOSES COMBINATION TABLE FOR OCULAR CARE 45

APPENDIX C : OCULAR DTC GROUPS AND DIAGNOSES IN THE DOT PRODUCT STRUCTURE 46

APPENDIX D : OCULAR CARE ACTIVITIES IN THE DOT PRODUCT STRUCTURE 48

APPENDIX E : CARE ACTIVITIES LINKING ALGORITHM 51

APPENDIX F : OCULAR CARE DTC GROUP DECISION TREE EXAMPLE 52

APPENDIX G : DESCRIPTION OF HOW THE APPLIED COGNITIVE TASK ANALYSIS WAS PERFORMED 53

APPENDIX H : TASK DIAGRAM CREATED DURING APPLIED COGNITIVE TASK ANALYSIS 55

APPENDIX I : COGNITIVE DEMANDS TABLE 61

APPENDIX J : USE CASE TEMPLATE USED IN THE STUDY 64

APPENDIX K : PACKAGE 1 “RECORD CONSULTATION” 65

APPENDIX L : PACKAGES 2 AND 3 “LINK CARE ACTIVITIES TO AN EC” 67

APPENDIX M : PACKAGE 5 “DIVIDE EC INTO SEGMENTS” 77

APPENDIX N : PACKAGE 4 “CLOSE ECS AND DEDUCE DTCS” 81

Summary Background — In 2005, a prospective payment system was introduced to Dutch healthcare,

the DBC. The system and its successor from 2012 (called DOT) are based on type of

diagnosis-related groups: DTCs (Diagnosis and Treatment Combinations). The proportion

of DTC for which health care organizations were not compensated for cost overruns

increased from 10% of all care provided in 2005 to 70% in 2012. Because of this change,

financial risk for health care organizations has increased in the past few years. To limit the

risk, it is essential that reimbursements accurately reflect the care provided. Our aim was

twofold in the context of the single medical specialty, Ocular Care; first, to design the

functional requirements of an information system that supports accurate reimbursements

determined with the DOT; second, to study the effect on reimbursements if clinicians were

supported by the information system we designed.

Methods — We captured the functional requirements of the information system with a use

case model by first performing requirements elicitation using a set of Cognitive Task

Analysis techniques. To study the change in reimbursements, we conducted a comparative

study of 2 samples of reimbursement data from 108 Ocular Care patients treated at the

Academic Medical Center in Amsterdam. Reimbursement amounts and their underlying

DTCs based on reimbursement data collected with the current practice for recording

reimbursement data (Sample 1) were compared to simulated reimbursement data if the

information system we designed had been used (Sample 2). We tested the differences in the

reimbursement amount between the 2 samples using the Wilcoxon signed-rank test, with

two-tailed p < 0.05 level as the threshold for statistical significance.

Results — We succeeded in designing a use case model of the information system; DOTIS.

The median reimbursement amount was for Sample 1: EUR 403 and for Sample 2: EUR 422.

The statistical test showed that the difference in median reimbursement amounts was not

statistically significant between the samples (p = 0.296), though we concluded that 22.5% of

the patients in our study had different DTCs in both samples.

Conclusion — We recommend further development of DOTIS and the development of

alternative solutions to support accurate DOT reimbursements. Furthermore, we believe

that further research is needed to better draw conclusions about the accuracy of DOT DTCs

and their reimbursements, preferably with a larger sample size. While the median difference

in reimbursement amounts was not significant, the cases where different DTCs were

observed suggests that further research into the accuracy of DOT DTCs is warranted.

Moreover, scientific literature on this topic is scarce.

Keywords — Reimbursements, DOT Prospective Payment System, Ocular Care, System

Design, Comparative Study

Samenvatting Achtergrond — In 2005 werd een prospectief betalingssysteem geïntroduceerd, het DBC

systeem. Het systeem en zijn opvolger de DOT, die in 2012 werd geïntroduceerd, zijn

gebaseerd op een type diagnose-gerelateerde groepen: DBC's (Diagnose en Behandeling

Combinaties). Het aandeel van de DBC waarvoor zorginstellingen niet werden

gecompenseerd voor kosten overschrijdingen van aangeboden zorg steeg van 10% in 2005

naar 70% in 2012. Als gevolg van deze verandering is het financiële risico voor

zorginstanties de afgelopen jaren toegenomen. Om dit risico te beperken is het essentieel dat

de vergoedingen nauwkeurig de geleverde zorg weerspiegelen. Ons doel was tweeledig

binnen het kader van een enkel medisch specialisme, de Oogheelkunde. Ten eerste, het

ontwerpen van functionele eisen voor een informatiesysteem dat accurate vergoedingen

ondersteunt die bepaald zijn aan de hand van DOT. Ten tweede, onderzoeken of de

vergoedingen mogelijk zouden veranderen wanneer artsen worden ondersteund door het

informatiesysteem dat wij hebben ontworpen.

Methode — We hebben de functionele eisen van het informatiesysteem vastgelegd met een

use case model door eerst een elicitatie van het eisen packet uit te voeren met behulp van een

set van cognitieve taak analyse technieken. Om de verandering in vergoedingen te

bestuderen hebben we een vergelijkend onderzoek uitgevoerd met behulp van twee

datagroepen van terugbetaling gegevens van 108 Oogheelkunde patiënten behandeld in het

Academisch Medisch Centrum in Amsterdam. De hoogte van de vergoedingen en hun

onderliggende DBC's gebaseerd op basis van vergoeding gegevens, verzameld met de

huidige manier van het registreren van vergoedingen gegevens voor Oogheelkunde patiënten

in het AMC, (groep 1) werden vergeleken met gesimuleerde vergoeding gegevens wanneer

het informatiesysteem dat wij hebben ontworpen wordt gebruikt (groep 2). De Wilcoxon

rank toets is gebruikt om het verschil in de hoogte van de vergoedingen te testen, met aan

beide zijden p <0.05 als drempel voor de statistische significantie.

Resultaten — We zijn erin geslaagd een use case model van het informatie system the

ontwikkelen; DOTIS. De mediane vergoedingen bedroegen respectievelijk EUR 403 voor

groep 1 versus EUR 422 voor groep 2. De statische test wees uit dat het verschil in de

mediane vergoedingen tussen beide groepen statistisch gezien niet significant is (p = 0.296).

Wel kunnen we concluderen dat 22,5% van de patiënten in ons onderzoek in beide groepen

verschillende DTC 's hadden.

Conclusie — Wij raden aan DOTIS verder te ontwikkelen en de ontwikkeling van

alternatieve oplossingen voor nauwkeurige DOT vergoedingen te steunen. Verder zijn wij

van mening dat er uitgebreider onderzoek nodig is om betere conclusies te kunnen trekken

Samenvatting vi

over de correctheid van de DOT DBC's en hun vergoedingen, bij voorkeur met een grotere

steekproef. Terwijl het mediane verschil in vergoedingen niet significant bleek te zijn

suggereren de gevallen waarin verschillende DBC's werden waargenomen dat nader

onderzoek naar de juistheid van de DOT DBC's gerechtvaardigd is. Bovendien is

wetenschappelijke literatuur over dit onderwerp schaars.

Trefwoorden —DOT , DBC PPS-systeem, Oogheelkunde, Systeem ontwerp, evaluatie

Abbreviations and Translations Table 1. Abbreviations and Translations

Abbreviation English Term Dutch Term

AMC Academic Medical Center in Amsterdam Het Academisch Medisch Centrum CA Care Activity Zorgactiviteit CARS CAs performed outside consultations N/a DBC The DBC Prospective Payment System Oude DBC-Systematiek DOT The DOT Prospective Payment System Nieuwe DBC-Systematiek DTC Diagnosis Treatment Combination DBC-Zorgproduct EC Episode Of Care Zorgtraject EC Segment Episode Of Care Segment Subtraject RSAD-Model Record and Extract to Deduce and Invoice

Model Registratie en Samenvatten naar Afleiden en Declareren Model

Sample 1 Sample consisting of data stored in the reimbursement information system, DBC registratie

N/a

Sample 2 Sample consisting of reimbursement data deduced and recorded by a Subject Matter Expert

N/a

UC Use Case N/a N/a Clinical Problem Zorgvraag N/a Diagnoses Combination Table Diagnose Combinatie Tabel N/a Dutch Casemix Office DBC Ondherhoud N/a Reimbursement Dataset Declaratiedataset N/a Segment Type Zorgtype

Eyða línu Eyða línu Eyða línu Eyða línu Eyða lí

n

Chapter 1

Introduction

Contents

1.1. CONTEXT .............................................................................................................................................. 2

1.2. RESEARCH QUESTIONS ....................................................................................................................... 3

1.3. OUTLINE OF THE THESIS ..................................................................................................................... 4

Our greatest weakness lies in giving up. The most certain way to succeed is

always to try just one more time.

[Thomas A. Edison]

2 Chapter 1. Introduction

1.1. Context

The Academic Medical Center in Amsterdam (AMC) is a 1002-bed university hospital in the

Netherlands [1], admitting 56.000 patients annually [2]. The hospital operates 34 inpatient

clinics, 21 outpatient clinics, and 5 day care units [3], all supported by an Electronic Health

Record, first implemented in 1998 [4]. Currently the Electronic Health Record consists of

multiple information systems; all designed to support the care processes within the AMC.

With it, users are able to perform tasks such as recording clinical data, scheduling

consultations, requesting laboratory tests, and recording data for reimbursement purposes

[5]. These information systems come from various software providers and to integrate them

a portal has been used since 2006; the AMC Zorgdesktop [3]. This approach has a limitation,

all the systems forming the current Electronic Health Record can barely communicate with

each other or interoperate [5]. With the limited interoperability between the Electronic

Health Record systems, the following developments are difficult to implement: 1) workflow

and clinical decision support, 2) planning of complex patient care, 3) support for

collaboration between care providers within the AMC and outside the organization, and 4)

reuse of data for e.g. research, management information and reimbursement [4].

In 2005, the DBC (Diagnose Behandel Combinatie, Dutch for Diagnosis Treatment

Combination) a prospective payment system was introduced for Dutch health care

organizations. The DBC was based on Diagnosis and Treatment Combinations (DTCs, in

Dutch DBC-Zorgproducten); type of diagnosis-related groups, or DRGs. It was intended to

determine reimbursements for medical treatments provided by health care organizations [6].

In the beginning of 2012 the DBC was replaced by a new version, the DOT (DBC's op weg

naar Transparantie, Dutch for DTCs towards Transparency) [7].

The DOT, like its predecessor, is based on DTCs [8]. For each DTC, a fixed fee is set,

that is used to reimburse a particular medical treatment provided in response to a patient's

need for care [6]. The DOT product structure includes approximately 4400 DTCs divided

into 123 groups [9], each group related to one of the 25 medical specialties the DOT covers

[10]. The DTCs are categorized into two segments; A and B. Segment A's DTC fees are

centrally set by the Dutch Healthcare Authority, whereas segment B's DTC fees are

determined through negotiations between health care organizations and insurers. For

segment A's DTCs, health care organizations have to operate within a fixed budget, but are

compensated for cost overruns. In contrast to segment A's DTCs, health care organizations

do not have a fixed budget ceiling for segment B's DTCs unless negotiated. However, they

are not provided with compensations in case of cost overruns of segment B's DTCs [11].

1.2. DOT Reimbursements in Ocular Care 3

In the AMC´s Electronic Health Record a set of reimbursement data items needs to be

collected in a reimbursement information system for DOT (and previous DBC)

reimbursement purposes. Part of these data items is clinical data recorded elsewhere in the

Electronic Health Record. However, this clinical data cannot be reused for reimbursement

purposes due to the limited interoperability of the AMC's Electronic Health Record and

predominant use of free text for clinical data recording. Therefore, clinicians need to

manually record this clinical data again in the reimbursement information system, leading to

data redundancy within the Electronic Health Record, and possibly discrepancies between

clinical data and reimbursement data.

Because of the redundant work, physicians are not motivated to completely and

consistently record reimbursement data. We believe that this incompleteness and

inconsistency essentially means that reimbursements for medical treatments have not been

accurately determined with the DBC.

While reimbursements are not accurate, the introduction of the DOT is a threat to health

care organizations since they take financial risk. This risk increased when DBC was replaced

by DOT because the B segment was extended. With the extension of the B segment, health

care organizations are not compensated for cost overruns of enlarged portion of DTCs in

the DOT product structure. When the DBC was introduced the size of the B segment was

10% of all DTCs. This gradually extended to 34% in 2009 [12], and in 2012 when DBC was

replaced by DOT it was extended to 70% [13]. Therefore, to limit the financial risk it is

essential that reimbursements accurately reflect the care provided.

1.2. Research Questions

Given that:

• DOT was introduced this year,

• the registration for financial reimbursement is causing data redundancy in the AMC's

Electronic Health Record and reimbursement system,

• reimbursements for medical treatments have not been accurately determined with the

DBC, and

• financial risk of health care organizations is increased with the DOT,

4 Chapter 1. Introduction

two research questions will be answered in this SRP. A large study, including all medical

specialties the DOT covers would be optimal. However, with no initial results to motivate it

a large study is not justifiable. Thus, before proceeding with a larger study we decided to

restrict ourselves to the context of single medical specialty, Ocular Care:

1. What are the functional requirements of an information system that supports accurate

DOT reimbursements (i.e. reimbursements determined with the DOT) in Ocular Care?

2. Is there a difference in the amount reimbursed and in underlying DTCs with DOT when

using two data sources for patients treated at the AMC's Ophthalmology Department:

a. reimbursement data that are stored in AMC's reimbursement information system

b. clinical data that are stored in the main clinical data registration system (Norma)?

Our hypothesis regarding research question 2 is that the reimbursed amount will be

greater in the case of clinical data stored in Norma. Furthermore, we hypothesize that in less

than half of the patient cases, the underlying DTCs will be identical.

1.3. Outline of the Thesis

In chapter 2 background information is presented. First, the scope of Ocular Care is defined.

Second, visual impairments in the Netherlands are described along with Dutch Ocular Care

in relation to the DBC both nationwide and in the AMC. Third, DOT reimbursements in

Ocular Care are described.

In chapter 3 the methods that were used to answer both research questions are featured.

In chapter 4 the results of both research questions are presented. First, for research

question 1, describing functional requirements of an information system that supports

accurate DOT reimbursements. Second, for research question 2, presenting answers to

whether there is a difference in the amount reimbursed and underlying DTCs with DOT in

the case of patients treated at the AMC's Ophthalmology Department based on two

different data sources.

In chapter 5 the discussions regarding this study are featured.

Chapter 2

Background Contents

2.1. DEFINITION OF OCULAR CARE 6

2.2. VISUAL IMPAIRMENTS AND OCULAR CARE 6

2.2.1. VISUAL IMPAIRMENTS AND OCULAR CARE IN THE NETHERLANDS 6

2.2.2. OCULAR CARE AT THE AMC 6

2.3. DOT REIMBURSEMENTS IN OCULAR CARE 8

2.3.1. RECORD 9

2.3.1.1. Parallel Episodes of Care 11

2.3.2. EXTRACT 12

2.3.3. DEDUCE 12

2.3.4. INVOICE 13

At a Glance

In this study, we focus on the use of the DOT prospective payment system to

determine reimbursements for Ocular Care. Therefore, in this chapter the scope

of Ocular Care will be defined (see 2.1). Furthermore, visual impairments in the

Netherlands are described along with Dutch Ocular Care in relation to the DBC

both nationwide and in the AMC (see 2.2). Finally, DOT reimbursements in

Ocular Care are described (see 2.3).

6 Chapter 2. Background

2.1. Definition of Ocular Care

As stated in the introduction, we restrict ourselves to the context of Ocular Care, which we

have chosen to define as medical treatments provided by ophthalmologists to patients with

ocular problems. We chose Ocular Care as we had access to a Subject Matter Expert in

Ocular Care with experience of the DBC.

2.2. Visual Impairments and Ocular Care

In this section, we will describe the prevalence of visual impairments in the Netherlands

along with Dutch nationwide Ocular Care in relation to the DBC (see 2.2.1). Thereafter,

Ocular Care at the AMC is described in relation to the DBC (see 2.2.2).

2.2.1. Visual Impairments and Ocular Care in the Netherlands

In the Netherlands, in 2008 it was estimated that 2 percent of the total population (311.000

individuals) were visually impaired, of whom 76.700 were blind, and 234.000 had low vision.

The leading causes for visual impairment in the Netherlands are: age-related macular

degeneration, cataracts, diabetic retinopathy, myopic degeneration, and refractive errors

[14]. These leading causes for visual impairments in the Netherlands are primarily diagnosed

in individuals over the age of 50 as well as those who are intellectually disabled. Of those

who were visually impaired, 174.000 (56%) suffered from visual loss, which was either

preventable or treatable. In most cases, individuals suffering from visual impairment do not

seek help at the onset of the impairment as they do not notice the loss of vision right away.

Assistance is either in the form of new glasses from an optician or treatment by an

ophthalmologist. In 2008, the waiting period for a consultation with an ophthalmologist was

a maximum of 6 months [14]. In 2007, the reimbursements for A and B section DTCs in

Ocular Care totaled € 210 million. To put these figures into context, these reimbursements

were circa 3% of all DBC reimbursements in that year, a total of € 7.095 billion [15].

2.2.2. Ocular Care at the AMC

In the AMC, Ocular Care is provided by its Ophthalmology Department. The department

is operated by a team of medical specialists in 14 full-time positions and support staff in 7.5

full-time positions. In 2009, the number of consultations was 40.629, the number of

surgeries was 2.401, and there were 3.644 inpatient days in the department. For DBC

reimbursements purposes, 13.810 ocular episodes of care (ECs, translation from Dutch of

"Zorgtrajecten") (see details on ECs in 2.3.1) were opened in 2009. The most frequent types

of diagnoses assigned to these ECs were other disorders in the eye or surroundings

(n=3.520), glaucoma (n=1.476), cataract (n=1.344), vitroretinal diseases (n=1.064), and

2.2. Visual Impairments and Ocular Care 7

diabetic oculopathy (n=1.060). Table 2 depicts ocular ECs opened at the AMC in 2009,

itemized by type of diagnoses assigned to them. [16]

Table 2. Diagnoses Types Assigned to Ocular ECs opened in 2009 at the AMC

Diagnoses Types Assigned to Episodes of Care Number of ECs (n)

Other disorders in the eye or surroundings 3.520

Pediatric ophthalmology 1.737

Glaucoma 1.476

Cataract 1.344

Vitreoretinal disease 1.064

Diabetic oculopathy 1.060

Orbit 689

Age-related macular degeneration 658

Medical retina 625

Cornea 510

Eyelids 348

Neurophthalmology 205

Lacrimal system disease 199

Uveitis 175

Strabismus 174

Ophthalmology, not profiled 26

Total 13.810


2.3. DOT Reimbursements in Ocular Care

DOT reimbursements are made according to the RSAD-model (van Registratie en

Samenvatten naar Afleiden en Declareren model, Dutch for Record and Extract to Deduce

and Invoice Model) for Ocular Care and other medical treatments provided by health care

organizations. In Figure 1 an overview of the RSAD-model [10] is presented. More details

on each step of the model will be described in the following subsections: Record (see 2.3.1),

extract (see 2.3.2), deduce (see 2.3.3), and invoice (see 2.3.4).

Figure 1. Overview of the RSAD-Model


2.3.1. Record

Recording of reimbursement data by a health care organization starts with the opening of an

EC in the health care organization's information system. This is done manually by the

treating ophthalmologist at the beginning of patient's medical treatment for a new clinical

problem (translation from Dutch of “Zorgvraag”). When an EC is opened, the following

occurs:

1. The treating ophthalmologist assigns an appropriate ocular diagnosis to the new clinical

problem.

2. The opening day of the EC is automatically recorded.

3. An EC segment (translation from Dutch of “Subtraject”) is automatically opened.

An EC segment is the basis of the reimbursement dataset (translation from Dutch of

“Declaratiedataset”) from which a single DTC is deduced. Care Activities (CAs, translation

from Dutch of “Zorgactiviteiten”) such as consultations, inpatient days, diagnostic tests, and

surgeries performed during an EC are recorded and linked to an EC segment (see 2.3.2 for

information on linking CAs), but within a single EC there can be one or more segments.

[17]

In an ocular EC (i.e. an EC opened by an ophthalmologist) two types (translation from

Dutch of “Zorgtype”) of EC segments are opened for care provided by ophthalmologist,

initial care1 and follow-up care2. The first segment in every EC is an initial care and later

segments are follow-up care. A follow-up care segment is opened the day after a previous

segment is closed. Figure 2 shows how the segment types are arranged within an EC. [17]

Figure 2. Ocular Episode of Care with Both Types of Segments

Three other types of EC segments are opened in an ocular EC. One type is in the case of

consults that are provided by non-ophthalmology medical specialists3. Another is for CAs

provided by non-ophthalmology medical specialists within in the same care providing

organizations as the ophthalmologist requesting it4. The third is for admission to intensive


care units5 [17]. We will not elaborate on these types of EC segments further nor include

them in our study as our focus is only on Ocular Care.

After an ocular EC is opened manually the opening and closing of segments, as well as the

closing of the EC, is done according to automated rules [17]:

1. Initial care segment without surgical CAs is closed 90 days after the segment's opening,

including the opening day (see Figure 3, for legend see Figure 6) [17].

2. Follow-up care segment without surgical CAs is closed 365 days after the segment's

opening, including the opening day (see Figure 4, for legend see Figure 6) [17].

3. An initial and follow-up care segment with surgical CAs is closed 43 days after the last

surgical CA is performed within the segment (see Figure 5, for legend see Figure 6) [17].

4. A segment is closed when the EC is closed [17].

In certain situations, exceptions to the above rules apply on when to close an EC segment

(see Appendix A) [17].

CAs

NS-CA

NS-CA

Segment Days 1 2 3 4 5 6 - 87 88 89 90 1 2 etc.

Figure 3. Closing of Initial Care Segment without Surgical Care Activities

CAs

NS-CA

NS-CA

NS-CA

Segment Days 1 2 3 4 5 6 - 362 363 364 365 1 2 etc.

Figure 4. Closing of Follow-Up Care Segment without Surgical Care Activities

CAs S-CA

S-CA

NS-CA

Segment Days 1 2 3 4 1 2 - 40 41 42 43 1 2 etc.

Figure 5. Closing of Initial and Follow-Up Care Segment with Surgical Care Activities

Legend: | Close segment = | Open a new segment = | NS-CA =Non-surgical CA | S-CA =Surgical CA

Figure 6. Legend for Figure 3, 4, and 5


The main rule on when to close an EC specifies that it should be closed 365 days after its

last segment with CAs linked to it has been closed. If a patient passes away while an EC is

open, the EC is closed at the date of death. At last, the treating ophthalmologist is allowed to

close an EC manually if no further CAs are planned [17].

2.3.1.1. Parallel Episodes of Care

While a single EC is open, parallel EC should be opened if the patient suffers from two

different ocular problems (see Figure 7), except when the two ocular problems appear

together in the Diagnoses Combination Table, a restrictive list of ocular diagnoses that are

required to be combined into one EC (see Appendix B [18]) [17].

Figure 7. Parallel Episodes of Care for Different Clinical Problem

Additionally, a parallel EC should be opened for the same ocular problem if an identical

surgical CA is performed on both eyes (see Figure 8). Exemptions to this rule do exist. An

additional EC is not opened if the surgical CAs are performed in a single operation and the

type of surgery is blepharoplasty6 (i.e. plastic surgery of an eyelid) or strabismus surgery7

[19].

Figure 8. Parallel Episodes of Care for the Same Clinical Problem in Both Eyes


2.3.2. Extract

To deduce DTCs for Ocular Care (see 2.3.3), the following recorded reimbursement data in

the health care organization's information system is needed as a part of a reimbursement

dataset for each EC segment [17]:

1. EC Information

a. Treating Medical Specialty; Ophthalmology

b. Ocular Diagnosis (see Appendix C for the list of ocular diagnoses in the DOT

product structure [20].)

2. Care Activities (see Appendix D for the list of Ocular CAs in the DOT product

structure defined by the chief clinical officer of the Ophthalmology Department in the

AMC).

a. Surgical Care Activities

b. Non-Surgical Care Activities

i. Diagnostic Care Activities

ii. Consultations

Health care organizations collect these data items by extracting them from their

information systems when an EC segment is closed. However, CAs can only be collected

directly if they are linked to an EC at the time they are recorded. When CAs are not linked

to an EC when recorded, it is necessary to do it retrospectively. Retrospective linking can be

done either with a manual process performed periodically or an automatic process when the

reimbursement data is collected. This automatic process is performed with a linking

algorithm (see Appendix E). The linking algorithm has a limitation as it cannot determine

each time to which episode of care a CA should be linked [21].

2.3.3. Deduce

A DTC can be deduced for a segment's medical treatment when all necessary data items

have been collected to a reimbursement dataset. In order to do so, the dataset is sent from

the health care organization's information system to an online grouping software, the

Grouper operated by the Dutch Casemix Office, a governmental organization responsible

for regulating and monitoring the DOT [22].

For datasets the Grouper receives it deduces DTCs and returns the result to the health

care organization's information system. To deduce a single DTC the Grouper uses decision

trees, following two steps [22]:


1. First the Grouper selects a DTC group based on the combination of treating medical

specialty and the diagnosis assigned to the EC [22]. In relation to ocular EC, in total 9

DTC groups exists in the DOT product structure with the treating medical specialty

listed as ophthalmology (see Table 3) [23]. Information on how the ocular diagnoses are

distributed between these 9 DTC groups can be found in Appendix C [20].

2. In the latter step, the Grouper deduces a DTC from the DTC group decision tree (see

example of a DTC group decision tree in Appendix F [24]) selected in the previous step

based on the CAs in the received dataset. It should be noted, that in some instances an

invalid DTC is deduced when it is not possible to establish a DTC based on the CAs in

the dataset [22].

Table 3. Ocular DTC Groups in the DOT Product Structure

DTC Group Description

Lens disorder Glaucoma Optic nerve or tract disorders Disorders of ocular muscles or binocular movement Visual impairments, blindness or accommodation and refraction disorders Other disorders in eye or surroundings Disorders of choroid / retina / vitreous / endophthalmitis Disorders of conjunctiva / sclera / cornea / iris / ciliary body / eyeball Disorders of eyelid / lacrimal apparatus / orbita

2.3.4. Invoice

When the health care organization's information system receives deduced DTCs from the

Grouper, reimbursements are calculated. Calculation is solely the responsibility of the health

care organization as results from the Grouper contain no DTC pricing information. To

calculate the reimbursements the health care organization's information system uses the fees

set for A and B segment DTCs (see 1.1).


1 Referred to as ZT11 in the DOT product structure [17].




5 Referred to as Zt51 and -52 in the DOT product structure [17].

6 CA code 31545 in the DOT product structure.

7 CA code 30941, 30942, 30943, 30989 in the DOT product structure.

Chapter 3

Methods Contents


3.1.1. REQUIREMENTS ELICITATION USING APPLIED COGNITIVE TASK ANALYSIS 16

3.1.2. DESIGNING FUNCTIONAL REQUIREMENTS 17


3.2.1. SUBJECTS 19

3.2.2. PROTOCOL DESIGN 19

3.2.2.1. Create Reimbursement Datasets 19

3.2.2.2. Deduce DTCs with Reimbursement Datasets 21

3.2.2.3. Calculate reimbursement for DTCs 21

3.2.3. OUTCOME MEASURES AND DATA ANALYSIS 21

3.2.4. ETHICAL CONSIDERATIONS 22

At a Glance

This chapter contains the methods we used to answer research questions

1 (in 3.1) and 2 (in 3.2).

16 Chapter 3. Methods

3.1. Support Accurate DOT Reimbursements in Ocular Care

To answer research question 1 “What are the functional requirements of an information

system that supports accurate DOT reimbursements in Ocular Care?” we first performed

requirements elicitation using a set of Cognitive Task Analysis techniques; Applied

Cognitive Task Analysis (see 3.1.1). Based on the information gathered with Applied

Cognitive Task Analysis we captured the functional requirements of the information system

with a use case model (see 3.1.2).

For this study we defined functional requirements as the activities (e.g. processing, data

manipulation, interaction with its users, and calculations) a system is required to complete

successfully.

3.1.1. Requirements Elicitation Using Applied Cognitive Task Analysis

We used Cognitive Task Analysis techniques; developed to describe and illustrate the human

cognitive skills, knowledge and the goal structure of processes being observed [29]; to elicit

the requirements for making DOT reimbursements for Ocular Care accurate. Specifically

we choose to use Cognitive Task Analysis techniques with the collective term Applied

Cognitive Task Analysis.

With the Applied Cognitive Task Analysis techniques we developed and observed a

structured and repeatable validation process of previously recorded Ocular Care's DOT

reimbursement data. We developed and observed this process based on a validation process

performed by our Subject Matter Expert, the chief clinical officer of the AMC's

Ophthalmology Department. The chief clinical officer performed this process routinely to

validate previously recorded Ocular Care's DBC reimbursement data. He has vast

experience in the clinical field including:

1. clinical experience (13 years practicing as an ophthalmologist),

2. experience of the Ocular Care processes within the AMC (worked at the AMC's

Ophthalmology Department for 17 years), and

3. experience of the DBC.

3.1. Support Accurate DOT Reimbursements in Ocular Care 17

The Applied Cognitive Task Analysis consisted of 4 techniques: Task Diagram Interview,

Knowledge Audit, Simulation Interview, and Cognitive Demands Table. Table 4 presents an

overview of each technique's aim and deployment. A detailed description of how we

performed the Applied Cognitive Task Analysis can be found in Appendix G.

Table 4. Overview of How the Applied Cognitive Task Analysis is Performed

Aim Deployment

1. Task Diagram Interview

Develop a surface-level look of the validation process, which would support that the DOT reimbursements for Ocular Care would be made accurately.

Model a task diagram of the process being observed based on interviews and observation of the Subject Matter Expert, as well as the rules to determine DOT Reimbursements in Ocular Care.

2. Knowledge Audit

Enhance the task diagram and assess the accurateness and validity of it. Furthermore, probe whether the domain knowledge and expertise of the Subject Matter Expert had led to specific strategies that were visible within the task diagram.

Use same sources as in the Task Diagram Interview to enhance the task diagram. Then validate the enhanced task diagram by interviewing the Subject Matter Expert.

3. Simulation Interview and Cognitive Demands Table

Analyze the sub-subtasks in the task diagram identified as requiring human cognitive skills and determine which can be automated without a human intervention or not.

Create a cognitive demands table of all the sub-subtasks requiring human cognitive skills and enlist which can be automated without a human intervention or not. Then validate the table by interviewing the Subject Matter Expert.

3.1.2. Designing Functional Requirements

Using the results from the Applied Cognitive Task Analysis; a task diagram (see

Appendix H) and a cognitive demands table (see Appendix I); we designed the functional

requirements of an information system supporting accurate DOT reimbursement.

Furthermore, information on the process's human cognitive skills (see Appendix I)

permitted us to design the information system with the aim to limit the need for those skills.

This was essential since at the AMC not all ophthalmologists were familiar with all rules

associated with recording of DOT reimbursement data and hence we speculated that

without support, they are not able to record DOT reimbursement data accurately.

Moreover, we presumed that within other care providing institutions offering Ocular Care

the situation was similar.

Before capturing the functional requirements we first decided where in the clinical

workflow the information system should support accurate DOT reimbursements for Ocular

Care, namely, when a treating ophthalmologist is recording clinical data during or after a


consultation with a patient. We made this decision as we wanted the information system to

be able to support:

1) Accurate DOT reimbursement for all Ocular Care provided by health care organizations

using the information system.

2) That the treating ophthalmologist would open an EC at the beginning of patient's

medical treatment for every new clinical problem and record all CA performed during an

EC.

3) That every CA would be linked to an EC when recorded, making retrospective linking

unnecessary (for details see 2.3.2).

To capture and describe the functional requirements we designed a use case model of the

information system. When designing the use case model we began by identifying the

“actors” and each of their overall goals that the information system would support. We then

designed the use cases by using use case template proposed by Cockburn (see Appendix H)

[25] with as single addition, a field for non-functional requirements. More specifically, only

non-functional requirements necessary to enable execution of the use cases under design.

Therefore, the non-functional requirements we specified were not intended to be a complete

listing of the non-functional requirements necessary to develop the information system.

We divided the model into use case packages, each consisting of use cases and a UML use

case diagram. To summarize the model we designed a UML package diagram and a

summary level use case.

3.2. Comparison of DOT Reimbursements in Ocular Care

To answer research question 2 “Is there a difference in the amount reimbursed and in

underlying DTCs with DOT when using two data sources for patients treated at the AMC's

Ophthalmology Department:

a. reimbursement data that are stored in AMC's reimbursement information system

b. clinical data that is stored in the main clinical data registration system (Norma)?

we performed a comparative study.

Our approach was to use the clinical data stored in Norma and simulate an information

system supporting accurate DOT reimbursements in Ocular Care. We compared these data

with the results of the current practice in recording reimbursement data for Ocular Care

provided to patients treated at the AMC's Ophthalmology Department.

3.2. Comparison of DOT Reimbursements in Ocular Care 19

In the following subsections, the study's methods will be described in more detail,

starting with the description of the subjects (see 3.2.1), then the protocol design (see 3.2.2),

the outcome measures and the data analysis techniques used (see 3.2.3), and finally the

ethical considerations (see 3.2.4).

3.2.1. Subjects

We conducted the study with data from 108 Ocular Care patients. The patients all started

treatment at the AMC's Ophthalmology Department in September 2011 and did not visit

the department earlier that year. The population consisted only of third-line patients who

had been referred to the department by an ophthalmologist outside the organization.

3.2.2. Protocol Design

To calculate the reimbursement amounts based on both data sources the following steps had

to be taken for each subject: 1) reimbursement datasets were created from reimbursement

data retrieved from both data sources, 2) DTCs were deduced from the reimbursement

datasets created, and 3) reimbursement were calculated for the DTCs deduced. These steps

are described in the following sub-sections.

3.2.2.1. Create Reimbursement Datasets

Reimbursement datasets (see 2.3.2) were created from reimbursement data retrieved from

both data sources. We retrieved reimbursement data that was recorded from the start day of

each patient's treatment until the end of the initial care type segment (see 2.3.1) of the EC

opened up at the beginning of their treatment. If a patient's initial care type segment ended

after 31st of December 2012 in either of the data sources, that patient was excluded from the

study.

Sample 1 reimbursement datasets consisted of reimbursement data stored in the

reimbursement information system, DBC registratie, containing reimbursement data

recorded with the current practice in recording these types of data at the Ophthalmology

Department in the AMC. These reimbursement data were recorded initially by the treating

ophthalmologist and a medical clerk at the Ophthalmology Department. Note that since

treating ophthalmologists did not open parallel ECs, Sample 1 did not contain any. A data

flow diagram is presented in Figure 9, describing how the reimbursement data was

transformed into Sample 1 reimbursement datasets.

Sample 2 reimbursement datasets consisted of reimbursement data deduced and recorded

from free text clinical data stored in Norma by a Subject Matter Expert, namely, the chief

clinical officer of the AMC's Ophthalmology Department. The clinical data stored in

Norma was captured initially by the treating ophthalmologist and a medical clerk. A data


flow diagram is presented in Figure 10, describing how the clinical data was transformed

into Sample 2 reimbursement datasets.

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Figure 9. Gane-Sarson Data Flow Diagram of Sample 1, Describing How the Reimbursement Data Was Transformed Into Sample 1 Reimbursement Datasets. Squares Represent People and Information Systems, Arrows Represent the Data and Their Flow, Rounded Rectangles Represent the Processes, and the Open-ended Rectangle the Data Store for Sample 1


Figure 10. Gane-Sarson Data Flow Diagram of Sample 2, Describing How the Reimbursement Data Was Transformed Into Sample 1 Reimbursement Datasets. Squares Represent People and Information Systems, Arrows Represent the Data and Their Flow, Rounded Rectangles Represent the Processes, and the Open-ended Rectangle the Data Store for Sample 2

3.2.2.2. Deduce DTCs with Reimbursement Datasets

To deduce DTCs from Sample 1 and 2 reimbursement datasets, we used a simulator of the

Grouper (see 2.3.3) “DBC zorgproducten tariefapplicatie” [26], a web application operated

by the Dutch Healthcare Authority. The reimbursement dataset for each initial care type

segment was entered manually into the DBC Zorgproducten Tariefapplicatie, which

deduced the appropriate DTC.

3.2.2.3. Calculate reimbursement for DTCs

For each deduced DTC its reimbursement amount was looked up in the AMC's DOT

DTCs standard price list1 [28]. With each DTC reimbursement amount known, the total

reimbursement was calculated for the Ocular Care provided to each subject based on both

Sample 1 and 2.

3.2.3. Outcome Measures and Data Analysis

We used descriptive statistics to describe Sample 1 and 2 reimbursements amounts (i.e. total,

maximum, minimum, IQR, median, upper quartile, and lower quartile). To answer whether


there was a difference in the reimbursement amount we compared reimbursements amounts

in Sample 1 and 2 using the Wilcoxon signed-rank test as the reimbursements followed

nonparametric statistical distributions.

We then created Venn diagram to analyze the proportion of subjects with identical and

different DTCs in Sample 1 and 2. We included in the analyze the total number of DTCs

deduced and number of subjects with parallel ECs in each set (i.e. “Sample 1 \ Sample 2”,

“Sample 2 \ Sample 1”, and “Sample 1 ∩ Sample 2”).

For the Wilcoxon signed-rank test we considered two-tailed p < 0.05 level as statistical

significance. In the Venn diagram 95% exact confidence intervals were calculated of the

proportion of subjects with identical and different DTCs in Sample 1 and 2. Data was

analyzed using the IBM SPSS Statistics 20.0.0 (Somers, NY, USA).

3.2.4. Ethical Considerations

The study did not affect subjects' treatment. Therefore, it did not require approval of the

Medical Ethics Committee at the AMC, nor patients' informed consent. All patient

identifiable information were deleted when the study was completed.

1 All health care organizations are obliged to publish a standard price list, which discloses the prices of A

and B segment DTCs that are used when patients are not health insured or their health insurer have not

negotiated on prices of B segments DTCs [27].

Chapter 4

Results Contents



4.2.1. DOT REIMBURSEMENTS 28

4.2.2. UNDERLYING DTCS OF THE DOT REIMBURSEMENTS 29

At a Glance

This chapter describes the results of research questions 1 (in 4.1) and 2 (in 4.2).

24 Chapter 4. Results

4.1. Support Accurate DOT Reimbursements in Ocular Care

We designed functional requirements for an information system that we gave the

hypothetical name “DOTIS”. We identified three “actors” of DOTIS: Ophthalmologist (i.e.

treating ophthalmologist), Grouper (i.e. the online grouping software operated by the Dutch

Casemix Office), and any type of registration system that health care organizations use to

record ocular CAs that are performed outside consultations (CARS), e.g. surgical CAs

performed during surgeries. Each of the actors overall goals is illustrated in the Actor-Goal

List in Table 5.

Table 5. Actor-Goal List for DOTIS

Name Goal

Ophthalmologist Record accurate DOT reimbursement data and link CAs to the appropriate ECs Grouper Deduce accurate DTCs from reimbursement datasets of closed ECs CARS Send CAs recorded with them to the DOTIS

The use case model of DOTIS consisted of 14 use cases in total: 1 summary level (see

UC-1 in Table 6), 8 user level, and 5 sub-function. For simplification and readability we

divided the use cases into 5 use case packages illustrated in the UML package diagram in

Figure 11. Summary of all the user level and sub-function use cases is presented in Table 7.

For details each package and its use cases are described in detail in Appendix K to Appendix

N.


Table 6. Use Case 1: Support Accurate DOT Reimbursements in Ocular Care

UC-1 Support DOT Reimbursements in Ocular Care

Primary Actor Ophthalmologist

Scope DOTIS

Level Summary

Precondition None

Minimal Guarantee

• See Success Guarantee.

Success Guarantee

• Ophthalmologist is supported in recording accurate reimbursement data, and DTCs are deduced from that data.

Main Success Scenario

1. Ophthalmologist opens DOTIS and selects a patient. 2. Ophthalmologist records a consultation (UC-2) and records clinical data in it (UC-3). 3. Ophthalmologist links consultation and its CAs to an EC (UC-4). 4. Ophthalmologist links CAs recorded with CARS to an EC (UC-8). 5. Grouper closes ECs and deduces DTCs (UC-14). 6. Health care organization invoices patient's insurers for medical treatments they have provided based on

deduced DTCs

Extensions

3a. Ophthalmologist detects that the consultation does not apply to any of the patient's open ECs in DOTIS 3a1. Ophthalmologist opens an EC to link consultation to (UC-5) during linking consultation and its

CAs to an EC (UC-4). 3b. Ophthalmologist detects that not all the CAs performed during the consultation apply to the same open

EC as the consultation. 3b1. Ophthalmologist links CAs occurring within a consultation to an EC (UC-7) during linking

consultation and its CAs to an EC (UC-4). 3b1a. Ophthalmologist detects that one or more CAs does not apply to any of the open ECs.

3b1a1. Ophthalmologist opens an appropriate EC to link CA to (UC-10) during linking CAs occurring within a consultation to an EC (UC-7)

4a. Ophthalmologist detects that one or more CAs does not apply to any of the open ECs. 4a1. Ophthalmologist opens an appropriate EC to link CA to (UC-10) during linking CAs occurring

within a consultation to an EC (UC-8).

Non-Functional Requirements

• Intermittent auto saves throughout the whole use case model • Ophthalmologist are able to view clinical data when recording reimbursement data in DOTIS


Figure 11. UML Package Diagram of DOTIS


Table 7. User Level and Sub-Function Use Cases in the DOTIS Use Case Model

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4.2. Comparison of DOT Reimbursements in Ocular Care

Of the 108 subjects we collected data from, 6 subjects were excluded as their initial care type

segments ended after 31st of December 2011 in either Sample 1 or 2. We deduced DTCs and

calculated reimbursement amounts from the reimbursement data of the remaining 102

subjects.

4.2.1. DOT Reimbursements

The Wilcoxon signed-rank test indicated that there was no significant difference in the

amount reimbursed with DOT (see Figure 12), two-tailed p = 0.296, in the case of patients

treated at the AMC's Ophthalmology Department based on:

Sample 1: Median = EUR 403, Total = EUR 82,513.

Sample 2: Median = EUR 422, Total = EUR 85,618.

Figure 12. Boxplot of Reimbursements for Ocular Care in the AMC with the DOT


4.2.2. Underlying DTCs of the DOT Reimbursements

In Figure 13 a Venn diagram is presented illustrating the proportion of subjects with

identical and different DTCs in Sample 1 and 2, as well as the total number of DTCs

deduced and number of subjects with parallel ECs in each set.

Figure 13. Venn Diagram of Proportion of Subjects with Identical and Different DTCs in Sample 1 and 2. Furthermore, Total Number of DTCs in Each Set and Which Contained Parallel ECs

Chapter 5

Discussions Contents

5.1. MAIN FINDINGS 32

5.2. TAKEAWAY MESSAGE FROM THIS STUDY 32

5.3. LIMITATIONS OF THE STUDY 33

5.4. THE RESULTS IN RELATION TO OTHER STUDIES 34

5.5. IMPLICATIONS 35

5.6. FUTURE RESEARCH 35

At a Glance

In this chapter we highlight the study's main findings (see 5.1), what we take

away from the study (see 5.2) limitations of the study (see 5.3), results in relation

to other studies (see 5.4), and implications (see 5.5). Lastly we present further

research we propose to be conducted (see 5.6).

32 Chapter 5. Discussions

5.1. Main Findings

In our two-folded study focusing on DOT reimbursements in Ocular Care we:

1) Designed a use case model of an information system that supported accurate DOT

reimbursements in Ocular Care, by first conducting requirement elicitation with

Applied Cognitive Task Analysis. The use case model captured the functional

requirement of the information system under design, DOTIS. Altogether the model

consisted of total 14 use cases: 1 summary level, 8 user level, and 5 sub-function.

2) Discovered that for Ocular Care patients in the AMC, referred by ophthalmologist

outside the AMC, the median DOT reimbursement amounts were not significantly

different (p = 0.296) based on:

a. Sample 1; reimbursement data recorded with current practice for Ocular Care

patients at the AMC (EUR 403).

b. Sample 2; simulated reimbursement data when an information system supporting

accurate DOT reimbursements in Ocular Care would be used (EUR 422).

3) Discovered that 22.5% (CI: 14.9% - 31.9%) of the Ocular Care patients, we studied the

difference in actual and simulated reimbursement amounts, had different DTCs in both

samples. Moreover, 6 of 102 patients had parallel ECs in Sample 2 while none in

Sample 1.

Both hypotheses we put forward regarding reimbursed amount and difference in

underlying DTC were rejected. The total reimbursement amount for all our subjects was

3.8% higher in Sample 2 (EUR 85,618) than in Sample 1 (EUR 82,513). However, this

difference was not statistically significant. Furthermore, by excluding the subject with

parallel ECs, the total reimbursement amount for all our subjects was 0.1% lower in Sample

2 (EUR 74,778) than in Sample 1 (EUR 74,858).

5.2. Takeaway Message from This Study

First, with the use case model of DOTIS we were able to: 1) summarize the impact of the

DOT prospective payment system on ophthalmologists work when an information system

is used to support them in recording accurate DOT reimbursement data, 2) show how every

CA would be linked to an EC when recorded, making retrospective linking (see 2.3.2)

obsolete, 3) present an information system that might guarantee accurate DOT

reimbursements if ophthalmologist would follow all its instructions and record

reimbursement data that reflected the care they provided.

Second, the Cognitive Task Analysis techniques were useful tools for the requirement

elicitation. Furthermore, they have been employed before when designing functional

5.3. Limitations of the Study 33

requirements for information systems demanding complex decisions making of their users

[30,31] similar to DOTIS.

Third, our results of the total reimbursement amount might be extrapolated to estimate

cumulative reimbursement amounts for the patient group we studied. For example by using

our results and estimate the total reimbursements with the same methods we applied, but in

the case of patients starting treatment in a single year instead of a month. The total

reimbursement amounts would be 37,260 EUR higher for the year 2011 by using

reimbursement data collected with:

1. An information system supporting accurate DOT reimbursements in Ocular Care

compared to

2. Current practice in recording reimbursement data for Ocular Care provided to patients

treated at the AMC's Ophthalmology Department.

5.3. Limitations of the Study

First, the list of Ocular CAs in the DOT product structure (see Appendix D) we used in our

study was not an official list from the Dutch Casemix Office, but a list from the chief clinical

officer of the AMC's Ophthalmology Department. We choose to use this list as the Dutch

Casemix Office does not publish a list of CAs used to deduce DTCs for each medical

specialty.

Second, no admission type CAs were in the list of Ocular CAs we used. Therefore,

neither when designing the use case model of DOTIS nor when conducting our comparative

study did we take admission type CAs into account. We recognized this as a minor

limitation despite the fact that it is possible to deduce DTCs from admission type CAs [26]

as the current practice in Ocular Care is to admit patients only if surgical or diagnostic CAs

are performed. In these cases DTCs are deduced on the basis of the surgical or diagnostic

CA and do not take admission type CAs into account [26].

Third, we only used one Subject Matter Expert when conducting the Applied Cognitive

Task Analysis, and reliability of information gathered from a single Subject Matter Expert

with this set of techniques has not been validated. However, it has been done with two

Subject Matter Experts [32]. Nevertheless, we believe that our information was valid as the

Subject Matter Expert was not the sole data source during the Applied Cognitive Task

Analysis as we also used information on how DOT reimbursements are made according to

the RSAD-model [10,17–23].


Fourth, in our comparative study we used a small sample population. The found

difference of 3.8% was not significant possibly because the study is underpowered. This

difference indicates that improvement in recording of reimbursement data is possible in

order to avoid revenue losses in the case of Ocular Care at the AMC. However, to confirm

that this study would need to be repeated with a larger sample population.

Fifth, to obtain reimbursement data for Sample 2 in our comparative study we employed

a single Subject Matter Expert. Therefore, Sample 2 might have contained incorrect data due

to mistakes or misinterpretations when the Subject Matter Expert deduced reimbursement

data from the clinical data. This limitation could have been avoided with 2 or more Subject

Matter Experts validating each other's works. This was not feasible in this study.

Sixth, we applied in our comparative study convenience sampling by selecting only third-

line patients who had been referred to the AMC's Ophthalmology Department by

ophthalmologists outside the organization. Therefore, the results cannot be generalized to all

Ocular Care patients in the AMC. Moreover, the sample population was expected to

comprise only of third-line patients. However, it could not be guaranteed as first- and

second-line patients could be defined as third-line patients in the database we collected the

sample population.

Seventh, the simulated reimbursement data might not accurately reflect reimbursement

data when recorded with DOTIS. We believe that DOTIS reimbursement data would better

reflect the Ocular Care provided than our simulated data. We base our believe on the fact

that data can be inaccurately recorded or incomplete in Electronic Health Records [34–41],

but with DOTIS that should be limited as DOTIS was designed to validate whether DOT

reimbursement data items are recorded and accurate.

Eight, in our study we did not measure the time spent in the current recording process,

but by attempting to guarantee that all reimbursement data is recorded with DOTIS will

possibly make recording of clinical data slower. Therefore, there is a chance it will affect the

clinical workflow. However, in the current process, diagnoses data are recorded multiple

times. When we can come to single registration and multiple uses, the total time of recording

clinical and reimbursement data will be lower.

5.4. The Results in Relation to Other Studies

By searching the PubMed database, we found no published studies on designing or

developing information systems, which support accurate recording of data for

reimbursement purposes, in particular for DOT and other prospective payment systems.

5.5. Implications 35

In their paper Benge et al. expect that by implementing structured data entry in the

Electronic Health Record of the Military Health System (U.S.) accuracy and completeness

of clinical data will be improved [42]. In regards to DOTIS we did not include in our design

user interface requirements. However, we do not believe it is sufficient to implement only

structured data entry to support accurate DOT reimbursements. To support it, DOTIS

needs to support that all necessary reimbursement data that are recorded as we designed the

system to do.

Concerning the effect of inaccurate recording of data for reimbursement purposes within

prospective payment systems we found evidence suggesting that it can cause both inaccurate

diagnosis-related grouping [43,44] and reimbursement [43,45]. These evidences indicate the

sense of urgency for supporting that accurate reimbursement data is recorded, which

DOTIS should do. Moreover, these evidences are similar to our findings regarding incorrect

diagnosis-related grouping given that we assume that the data in Sample 2 is the “gold

standard”. Based on this assumption we discovered that in 22.5% of the 102 cases, diagnosis-

related grouping was inaccurate when reimbursement data was recorded with the current

practice in Ocular Care at the AMC.

5.5. Implications

Although DOTIS was designed for Ocular Care it might be used as a framework for

designing information systems with same objectives for other medical specialties because

DOT reimbursements are fundamentally determined in the same way for all medical

specialties [17].

Despite the fact that we did not find a significant difference in DOT reimbursement

amounts, our other findings and the fact that prices of DTCs can be changed at any given

time, lead us to the conclusion that in the case of AMC's Ocular Care the risk of revenue

loss is real. Finally, if reimbursement data is not accurate, it might not only have effect on

funding of medical treatments, but as well on other areas using the data such as health

administration and studies in health sciences.

5.6. Future Research

We recommend further development of DOTIS and development of alternative solutions to

support accurate DOT reimbursements (i.e. information systems, training programs in

recording of reimbursement data for clinicians, and retrospective validation of data

recorded) whether it is for single or all medical specialties which DOT covers. If DOTIS or

any of these solutions will be developed the following aspects should be studied:


1) the accuracy of the DTCs, 2) effect on reimbursements for medical treatments, 3) user

acceptance, 4) effect on clinical workflows, and 5) cost benefit analysis for using them.

In light of our findings, small sample population, and narrow scope of our comparative

study and the fact that scientific literature on this topic is scarce, we recommend that further

research will be undertaken to better conclude the accuracy of DOT DTCs and their

reimbursements. Then preferably with a larger sample size and both in the case of Ocular

Care and other medical specialties.

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Appendices Contents

APPENDIX A : EXEMPTION RULES FOR CLOSING OCULAR EC SEGMENT 44

APPENDIX B : DIAGNOSES COMBINATION TABLE FOR OCULAR CARE 45

APPENDIX C : OCULAR DTC GROUPS AND DIAGNOSES IN THE DOT PRODUCT STRUCTURE 46

APPENDIX D : OCULAR CARE ACTIVITIES IN THE DOT PRODUCT STRUCTURE 48

APPENDIX E : CARE ACTIVITIES LINKING ALGORITHM 51

APPENDIX F : OCULAR CARE DTC GROUP DECISION TREE EXAMPLE 52

APPENDIX G : DESCRIPTION OF HOW THE APPLIED COGNITIVE TASK ANALYSIS WAS PERFORMED 53

APPENDIX H : TASK DIAGRAM CREATED DURING APPLIED COGNITIVE TASK ANALYSIS 55

APPENDIX I : COGNITIVE DEMANDS TABLE 61

APPENDIX J : USE CASE TEMPLATE USED IN THE STUDY 64

APPENDIX K : PACKAGE 1 “RECORD CONSULTATION” 65

APPENDIX L : PACKAGES 2 AND 3 “LINK CARE ACTIVITIES TO AN EC” 67

APPENDIX M : PACKAGE 5 “DIVIDE EC INTO SEGMENTS” 77

APPENDIX N : PACKAGE 4 “CLOSE ECS AND DEDUCE DTCS” 81

44 Appendices

Appendix A: Exemption Rules for Closing Ocular EC Segment

Table 8. Exemption Rules for Closing Ocular EC Segment

Exemption Situation & Rule

1 Situation: Two or more intravitreal administrations (39810†) are linked to an EC and it has one of the following diagnoses assigned to it: 1. Posterior uveitis / Pan-uveitis (503‡) 2. Other vitreous pathology (609‡) 3. (Chorio) retinitis / Vasculitis (652‡) 4. Retinopathy (excl. DRP) (655‡) 5. Vascular closure (657‡) 6. Other retina pathology(659‡) 7. Subretinal neovascularization (704‡)

8. Maculopathy (705‡) 9. Macular degeneration (707‡) 10. Other macular pathology (709‡) 11. NPDRP (754‡) 12. Preproliferative DRP (755‡) 13. PDRP (757‡) 14. Other DRP pathology (759‡)

Rule: The day before a follow-up intravitreal administration is performed an EC segment is closed and another opened the subsequent day.

2 Situation: Two or more photodynamic treatments (39076†) are linked to an EC and it has one of the two following diagnoses assigned to it:

1. Other retina pathology (659‡) 2. Subretinal neovascularization (704‡) Rule:

The day before a follow-up photodynamic treatment is performed an EC segment is closed and another opened the subsequent day.

3 Situation: Two or more surgical treatments for strabismus (30941†, 30942†, 30943†, 30989†) are linked to an EC and it has one of the following diagnoses assigned to it:

1. Concomitant strabismus (204‡) 2. Incomitant strabismus (205‡)

3. Other impairment of binocular function (209‡)

Rule: The day before a follow-up surgical treatment for strabismus is performed an EC segment is closed and another opened the subsequent day.

4 Situation: Two or more treatments for retinal defect / retinal detachment (30895†, 30896†, 31296†, 31297†, 31347†,) are linked to an EC which has retinal defect / retinal detachment (654‡) diagnosis assigned to it.

Rule: The day before a follow-up treatment for retinal defect / retinal detachment is performed an EC segment is closed and another opened the subsequent day.

† CA code in the DOT product structure ‡ Diagnostic code in the DOT product structure

Appendix B: Diagnoses Combination Table for Ocular Care 45

Appendix B: Diagnoses Combination Table for Ocular Care

Table 9. Diagnoses Combination Table for Ocular Care

Diagnosis 1 Diagnosis 2

Code‡ Description Code‡ Description

101 Non-ocular pathology 101 Non-ocular pathology 102 Past pathology 102 Past pathology

103 Risk of eye disease 103 Risk of eye disease

107 Systemic disease without ocular pathology 107 Systemic disease without ocular pathology

151 Visual impairments, unknown cause 151 Visual impairments, unknown cause

151 Visual impairments, unknown cause 155 Refractive anomalies

154 Amblyopia 154 Amblyopia

154 Amblyopia 155 Refractive anomalies

155 Refractive anomalies 151 Visual impairments, unknown cause

155 Refractive anomalies 154 Amblyopia

155 Refractive anomalies 155 Refractive anomalies

155 Refractive anomalies 159 Other visual impairments

204 Concomitant strabismus 204 Concomitant strabismus

205 Incomitant strabismus 205 Incomitant strabismus

209 Other impairment of binocular function 209 Other impairment of binocular function

255 Dermatochalasis 255 Dermatochalasis

402 Infectious conjunctivitis 402 Infectious conjunctivitis

403 Allergic conjunctivitis 403 Allergic conjunctivitis

751 No DRP 751 No DRP

751 No DRP 754 NPDRP

754 NPDRP 751 No DRP

854 Intracranial pathology 854 Intracranial pathology

859 Other neuro-ophthalmology 859 Other neuro-ophthalmology

951 No diagnosis 951 No diagnosis

960 Peer consulting 960 Peer consulting

‡ Diagnostic code in the DOT product structure

46 Appendices

Appendix C: Ocular DTC Groups and Diagnoses in the DOT Product

Structure

Table 10. Ocular DTC Groups and Diagnoses in the DOT Product Structure 1⁄2

DTC Diagnostic Code†† Diagnosis Description in Dutch

Group Code†† Group Description

79699 Other disorders in eye or surroundings

101 Geen oogheelkundige pathologie 102 Doorgemaakte pathologie

103 Risico op oogaandoening

107 System aand znd ooghlk path

79599 Visual impairments, blindness or accommodation and refraction disorders

151 Visusstoornis e.c.i. 154 Amblyopie

155 Refractie-anomalie

159 Overige visusstoornis

79499 Disorders of ocular muscles or binocular movement

204 Concomitant scheelzien 205 Incomitant scheelzien

209 Overige afwijkingen binoculaire functie

79999 Disorders of eyelid / lacrimal apparatus / orbita

251 Verworven ptosis 252 Congenitale ptosis

253 Blepharitis

255 Dermatochalazis

257 Ec- en entropion

258 Chalazion|hordeolum

259 Overige pathologie oogleden

303 Ontsteking

306 Obstructie (congenitaal)

307 Obstructie (verworven)

309 Overige pathologie traanwegen

352 Graves' orbitopathie

353 Infectie | ontsteking

358 Orbita tumor

359 Overige pathologie orbita

79899 Disorders of conjunctiva / sclera / cornea / iris / ciliary body / eyeball

402 Infectieuze conjunctivitis 403 Allergische conjunctivitis

404 Sicca syndroom

407 Pterygium

409 Overige pathologie conjunctiva

452 Keratitis

454 Corneaerosie | corp.alienum

456 Perforatie, alleen cornea

457 Corneadystrofie | keratoconus

459 Overige pathologie cornea

79799 Disorders of choroid / retina / vitreous / endophthalmitis

502 Uveitis anterior

503 Uveitis posterior | panuveitis

509 Overige pathologie uvea

70401 Lens disorder 554 Cataract 557 Nastaar 559 Overige pathologie lens

†† Code in the DOT product structure

Appendix C: Ocular DTC Groups and Diagnoses in the DOT Product Structure 47

Table 11. Ocular DTC Groups and Diagnoses in the DOT Product Structure 2⁄2

DTC Diagnostic Code††

Diagnosis Description in Dutch

Group Code†† Group Description

79799 Disorders of choroid / retina / vitreous / endophthalmitis

603 Endophthalmitis 604 CV bloeding

607 CV troebeling | CV loslating

609 Overige pathologie CV

652 (chorio)Retinitis / vasculitis

654 Retinadefect | retinaloslating

655 Retinopathie (excl. Drp)

657 Vaatafsluiting

659 Overige pathologie retina

704 Subretinale neovascularisatie

705 Maculopathie

707 Maculadegeneratie

709 Overige pathologie macula

751 Geen DRP

754 Npdrp

755 Preprolif. Drp

757 Pdrp

759 Overige pathologie DRP

79899 Disorders of conjunctiva / sclera / cornea / iris / ciliary body / eyeball

802 Episcleritis 806 Perfor(> of anders co.perfor)

809 Overige pathologie bulbus | sclera

70801 Optic nerve or tract disorders 852 Opticopathie

854 Intracraniele pathologie

859 Overige neuro-ophthalmologisch

70601 Glaucoma 901 Glaucoom risico | ocul.hypertensie 904 Primair glaucoom

907 Secundair glaucoom

909 Overige glaucoom

79699 Other disorders in eye or surroundings 951 Geen diagnose

954 Congenitale oogafwijking

959 Overige oogafwijkingen


48 Appendices

Appendix D: Ocular Care Activities in the DOT Product Structure

Table 12. Ocular Surgical Care Activities 1⁄3

CA Code†† CA Description in Dutch

30901 Foto-dynamische therapie.

31515 Verwijderen aandoening ooglid (bijv tumor).

31400 Verwijdering van een of meerdere corpora aliena subconjunctivaal.

30896 Coagulatie van intra-oculaire aandoeningen d.m.v. focale laserbehandeling.

30897 Coagulatie van intra-oculaire aandoeningen d.m.v. panretinale laserbehandeling.

31625 Inbrengen plug punctum lacrimale.

39805 Subconjunctivale injectie van medicatie.

30898 Behandeling van intra-oculaire aandoeningen d.m.v. YAG-laser.

31293 Voorsegmentsvitrectomie.

31242 Cataractoperatie extracapsulair.



31280 Inbrengen van kunststoflens.

31251 Cataractoperatie intracapsulair.

31250 Cataractoperatie intracapsulair.

31201 Nastaardiscisie.

31282 Operatieve repositie van een geluxeerde kunststoflens.

31281 Het verwijderen van een kunststoflens.

31243 Verwijdering van geluxeerde lens.

31020 Hoornvlieshechting.

31032 Perforerende hoornvliestransplantatie.

31044 Natrium-EDTA spoeling van het hoornvlies.

31019 Overhechting ulcus cornea.

31041 Verwijdering van een of meerdere corpora aliena.

31043 Tatouage van het hoornvlies.

31033 Voorste lamellaire hoornvliestransplantatie (diepe anterieure lamellaire keratoplastiek (DALK)).

31034 Achterste lamellaire hoornvliestransplantatie (posterieure lamellaire keratoplastiek (PLK).

31013 Verwijderen van een of meerdere tumoren van de cornea met plastiek.

31014 Verwijdering van een of meerdere tumoren van de cornea zonder plastiek.

31130 Herstel iridodialysis.

31131 Herstel prolapsus iridis.

31128 Iridotomie of iridectomie.

31151 Losmaken iris van cornea.

31133 Maken van nieuwe pupil-opening.

31122 Verwijdering van iriscyste of iristumor.

31138 Glaucoom operatie.

31139 Filtrerende operatie voorste oogkamer met plaatsen filterimplant.

31140 Goniotomie.

31072 Sclerahechting.

31071 Plaatsen radioactieve plaque sclera.

31548 Correctie ptosis wenkbrauw - endoscopisch.

31545 Blepharoplastiek.

31550 Canthusreconstructie.

31521 Correctie floppy eyelid. 31547 Correctie ptosis wenkbrauw - extern.


Appendix D: Ocular Care Activities in the DOT Product Structure 49



31519 Desinsertie oogspieren bovenooglid.

31530 Ectropion operatie.

31544 Electrische epilatie van oogharen.

31531 Entropion operatie.

31563 Fasanella-servat procedure.

31561 Frontalis suspensie.

31523 Herstel laceratie ooglid.

31517 Implantatie goudgewichtje in bovenooglid.

31562 Levator plastiek.

31522 Operatieve behandeling blepharospasme.

31591 Opheffen van de verkleining van de ooglidspleet respectievelijk van een gesloten lidspleet.

31538 Verkleining lidspeet.

31511 Verwijdering van een of meer chalazia per zitting. 31516 Verwijderen of correctie aandoening ooglid inclusief reconstructie met zwaailap of trans- of

implantaat.

31518 Fornix verdiepende hechtingen.

30895 Coagulatie van intra-oculaire aandoeningen.

30909 Enucleatio bulbi.

30914 Evisceratio bulbi.

30920 Primaire behandeling van ernstige perforerende verwondingen van de oogbol.

30931 Verwijdering van een of meerdere intra-oculaire corpora aliena.

30821 Exenteratio orbitae.

30851 Operatieve behandeling orbita bodemfractuur.

30803 Operatieve decompressie van de orbita. 30823 Orbitectomie (operatief verwijderen afwijking(en) uit de orbita inclusief verwijderen (delen van)

de benige oogkas).

30805 Anterieure orbitotomie.

30804 Laterale orbitotomie.

30943 Scheelzienoperatie paralytisch.

30942 Scheelzienoperatie schuine oogspieren.

30941 Scheelzien operatie.

30989 Vier spieren operatie.

39810 Intravitreale injectie van medicatie.

39430 Biopsie met incisie intra-oculaire structuur.

39431 Biopsie met incisie extra-oculaire structuur.

31640 Maken van een verbinding tussen neus en conjunctivaalzak

31657 Dacryo-cysto-rhinostomie - endonasaal (En-DCR

31656 Dacryo-cysto-rhinostomie - uitwendig (Ex-DCR

39821 Doorgankelijkheidstest traanwegen (ANEL-test).

31621 Herstel traanpunt.

31663 Reconstructie canaliculus.

31620 Sondage van een of meerdere traanwegstenosen.

31638 Verwijderen van een traanzak.

31450 Opheffen symblepharon met transplantatie.

31492 Opheffen symblepharon zonder transplantatie.

31423 Verwijdering van een of meerdere tumoren van de conjunctiva.

31424 Verwijdering van een of meerdere tumoren van de conjunctiva zonder plastiek.

31451 Vrije plastiek met lip of ander slijmvlies.


50 Appendices



31297 Pars plana vitrectomie bij behandeling van ablatio retinae incl. verwijderen tractiemembranen.

31295 Pars plana vitrectomie.

31296 Pars plana vitrectomie bij behandeling van ablatio retinae.

31298 Verwijderen siliconenolie. 31347 Behandeling ablatio retinae middels uitwendige techniek.


Table 15. Ocular Non-Surgical Care Activities, Consultations


190011 Eerste polikliniekbezoek 190013 Herhaal-polikliniekbezoek(en) bij een lopende DBC


Table 16. Ocular Non-Surgical Care Activities, Diagnostic Activities


39803 Aangezichtsfotografie.

39822 Biometrie oogbol.

39483 Diagnostische echografie van het oog.

39446 Injectie botulinetoxine.

39825 Cornea topografie.

39812 Donkeradaptatie-curve.

39826 Endotheelfotografie.

39722 Electro-oculografie (EOG).

39788 Eenvoudige electro-retinografie (ERG)

39820 Fundusfotografie

39824 Zenuwvezel analyse (HRT

39819 Gezichtveldsonderzoek.

39916 Het volledig aanpassen en voorschrijven van contactlenzen.

39515 Fluorescentie-angiografie.

39817 Uitgebreid kleurenzien-onderzoek.

39808 Low-vision onderzoek en therapie gedurende een jaar.

39823 Optische coherentie tomografie (OCT).

39814 Voortgezette orthoptische behandeling per bezoek (binoculair).

39813 Eerste orthoptisch onderzoek (binoculair).

39827 Pachymetrie.

39816 Prematurenretinopathie (ROP) screening.

39766 Visual evoked response (VER)

39802 Voorsegmentfotografie.

39828 Afnemen corneakweek.

39809 Diagnostische glasvochtpunctie.

39811 Fundoscopie + voorsegment-onderzoek onder narcose + eventuele oogdrukmeting.

39815 Diagnostische voorste oogkamerpunctie.

30825 Biopsie orbita.

81093 MRI hersenen - standaard. 82042 CT onderzoek van de aangezichtsschedel


Appendix E: Care Activities Linking Algorithm 51

Appendix E: Care Activities Linking Algorithm

Annotation: This linking algorithm is only applicable only when CAs are being linked to ocular ECs that

contain only initial and follow-up care segments.

1. If a CA is being performed by an ophthalmologist and a single ocular EC is open, then

the CA is linked automatically to the segment within the open EC, which was open on

the day the CA was performed.

2. If no medical specialty is recorded as having performed a CA, or the medical specialty

performing the CA does not have an open EC when the activity is performed, then the

activity is linked automatically to the open EC of the medical specialty requesting the

activity (i.e. treating medical specialty), which in this case is ophthalmology. Within the

EC, the activity is linked to the segment, which was open at the day the CA was

performed.

3. When a patient is simultaneously being treated by ophthalmology and one or more other

medical specialties and each one of them has a single EC open, a CA performed during

that time is linked automatically to the most recently opened EC.

4. If two or more ocular ECs are open simultaneously and a CA is performed during the

time when the ECs intersect, the CA cannot be linked automatically to an EC segment

(see Figure 14). Instead, a manual choice must be made as to which EC and its segment

the activity should be linked.

Figure 14. Parallel ECs in the same medical specialty and the care activity linked to them

5. If the rules in situations 1, 2, 3, and 4 are not sufficient to link a CA to an EC segment an

error is reported. The error will state that it was not possible to link a CA to an EC

segment automatically. If this error occurs, the only way to link a CA to an EC segment

is by doing so manually.

Appendix F: Ocular Care DTC Group Decision Tree Example

DTC Group: “Visual impairments, blindness or accommodation and refraction disorders“

Note: Each end node in the decision tree represents a single DTC.

Appendix G: Description of How the Applied Cognitive Task Analysis was Performed 53

Appendix G: Description of How the Applied Cognitive Task Analysis

was Performed

1. Task Diagram Interview

With the Task Diagram Interview we developed a surface-level look of the validation

process that would support that the DOT reimbursements for Ocular Care would be made

accurately. Because this process was not formalized, the Task Diagram Interview also aimed

to elicit its goal structure and formalize it. Furthermore, the aim was to elicit were human

cognitive skills were needed in the process. With these aims a task diagram was created of the

process being observed. To model the task diagram, a process matrix was used were tasks

were ordered into subtasks and sub-subtasks and numbered accordingly. Each sub-subtask

was described and defined whether it needed a human cognitive skill.

The task diagram was created with the use of information from three sources. The first

source was the rules to determine DOT Reimbursements in Ocular Care (see 2.3). The

second source was unstructured interviews with the Subject Matter Expert, which took place

from November 2011 until February 2012. In which questions were asked about the DOT

and the process he routinely performed to validate Ocular Care's DBC reimbursement data.

The third source was screen and audio recordings of two observations of the Subject Matter

Expert describing how he would carry out the validation process if structured and

repeatable.

2. Knowledge Audit

The goal of the Knowledge Audit by means of an interview with the Subject Matter Expert

conducted in March 2012 was twofold, to assess

1) the accuracy and validity of the task diagram in view of the non-native Dutch

background of the primary researcher.

2) whether the domain knowledge, and skill of the expert have led to specific strategies

that were visible within the task diagram.

An human-computer interaction expert conducted the interview with the Subject Matter

Expert in his native language Dutch. An audio recording was made of the interview and

notes were taken. Supported by screenshots of the information system, which the Subject

Matter Expert would have used during the formalized validation process, the human-

computer interaction expert guided the Subject Matter Expert through the task diagram task

by task. When guided through the task diagram the Subject Matter Expert was first asked to

describe the task as depicted in the task diagram. Then the Subject Matter Expert was probed

on the basis of his skill and domain expertise to explain why each task belonged to the

54 Appendices

process, and if it was accurately described in the Task Diagram. Moreover, questions were

asked if any tasks were missing from the diagram that should be part of the formalized

validation process. In case, the Subject Matter Expert found an error in the task diagram or

identified missing tasks, relevant adjustments were made to the task diagram.

During the Knowledge Audit, before the interview in March 2012 with the Subject Matter

Expert took place the task diagram was enhanced. To enhance the task diagram the

following information were incorporated by using the same information sources that were

used to develop the initial version of the task diagram during the Task Diagram Interview:

1) Which data was needed during the validation, when was it needed and from which

information sources origins the data.

2) When in the process was it necessary to temporary record data to avoid mistakes

during the validation. Moreover, which information was recorded.

3) When in the process was it necessary to use the temporary data. Moreover, which

data was used.

4) In which information systems were data needed to be entered and/or altered, and

when it occurred and then which data was involved.

3. Simulation Interview and Cognitive Demands Table

With the Simulation Interview the objective was to analyze the sub-subtasks in the task

diagram identified as requiring human cognitive skills by developing a Cognitive Demands

Table and interviewing the Subject Matter Expert for the last time.

Prior to interviewing the Subject Matter Expert, the primary investigator and the human-

computer interaction expert created a Cognitive Demands Table. In it all the sub-subtasks

from the task diagram identified as requiring human cognitive skills were collected. For each

of those steps it was determined whether it could be automated without a human

intervention or not. Furthermore, it was postulated what kind of human errors could occur

during each step and what should be the strategies to avoid these errors.

The interview with the Subject Matter Expert was conducted in the same settings as the

Knowledge Audit interview. During it the Subject Matter Expert was asked if he agreed on

how each step had been classified. Moreover if he had anything to add to what had been

postulated regarding human errors and strategies to avoid them. In case the Subject Matter

Expert disagreed or had anything to add, relevant adjustments were made to the Cognitive

Demands Table.

Appendix H: Task Diagram Created during Applied Cognitive Task Analysis

Table 17. Task 1: Initial Care Type Segment's Default Opening Period and Consultations within It

Sub

-Su

bta

sk

Tas

k D

escr

ipti

on

D

ata

Sou

rce

Inp

ut

Ou

tpu

t H

um

an

cogn

itiv

e sk

ills

Sub

task

1.1

: F

ind

an

d v

alid

ate

init

ial

care

ty

pe

segm

ent'

s st

art

dat

e 1.

1.1

1) F

ind

in

itia

l ca

re t

yp

e se

gmen

t's

star

t d

ate

in

No

rma

fou

nd

in

: Ove

rvie

w t

ab -

˃To

p r

igh

t co

rner

-

˃Co

nsu

ltat

ion

s -

˃Dat

e o

f h

rst

con

sult

atio

n

2) F

ind

EC

in

itia

l seg

men

t's

star

t d

ate

in D

BC

re

gist

rati

e fo

un

d i

n: L

ist

of

Ver

rich

tin

gen

- ˃ F

irst

C

on

sult

atio

n -

˃Dat

e p

erfo

rmed

3) V

alid

ate

if t

he

init

ial c

are

typ

e se

gmen

t's

star

t d

ate

reco

rded

in

th

e D

BC

reg

istr

atie

is

iden

tica

l to

th

e o

ne

reco

rded

in

No

rma

No

rma;

DB

C

regi

stra

tie

Init

ial

care

ty

pe

segm

ent'

s' s

tart

d

ate

Star

t d

ates

of

the

init

ial

care

ty

pe

segm

ents

re

cord

ed i

n t

he

No

rma

and

th

e D

BC

reg

istr

atie

are

th

e sa

me

= T

RU

E o

r F

AL

SE

x

1.1.

2

Tw

o a

ctio

ns

po

ssib

le b

ased

on

wh

eth

er t

he

ou

tpu

t fr

om

1.1

.1 i

s T

RU

E o

r F

AL

SE. S

ee t

he

1.1.

2 o

utp

ut

for

the

acti

on

s.

1.1.

1 O

utp

ut

IF T

RU

E:

No

act

ion

E

LSE

: C

han

ge t

he

init

ial

care

ty

pe

segm

ent'

s st

art

dat

e in

th

e D

BC

reg

istr

atie

so

it

corr

esp

on

ds

to t

he

init

ial

care

ty

pe

segm

ent'

s st

art

dat

e re

cord

ed i

n N

orm

a

Sub

task

1.2

: D

efin

e th

e in

itia

l ca

re t

yp

e se

gmen

t's

def

ault

o

pen

ing

per

iod

an

d s

earc

h f

or

con

sult

atio

ns

wit

hin

it.

1.2.

1

1) C

alcu

late

def

ault

in

itia

l ca

re t

yp

e se

gmen

t's

end

dat

e (=

in

itia

l ca

re t

yp

e se

gmen

t's

star

t d

ate

+

89 d

ays)

2)

Def

ine

the

init

ial

care

ty

pe

segm

ent'

s d

efau

lt

op

enin

g p

erio

d (

= f

rom

in

itia

l ca

re t

yp

e se

gmen

t's

star

t d

ate

to e

nd

dat

e) a

nd

rec

ord

in

a

tem

po

rary

sto

rage

D

BC

re

gist

rati

e

Init

ial

care

ty

pe

segm

ent'

s st

art

dat

e In

itia

l ca

re t

yp

e se

gmen

t's

def

ault

op

enin

g p

erio

d

x

1.2.

2

1) I

den

tify

in

th

e o

verv

iew

of

all

pat

ien

t's

con

sult

atio

n a

nd

th

eir

dat

es i

n N

orm

a ex

cl.

ph

on

e co

nsu

ltat

ion

s an

d r

emar

ks

(fo

un

d i

n:

Ove

rvie

w t

ab -

˃ To

p r

igh

t co

rner

- ˃C

on

sult

) w

hic

h a

re w

ith

in t

he

init

ial c

are

typ

e se

gmen

t's

def

ault

op

enin

g p

erio

d r

eco

rded

in

1.2

.1

2) R

eco

rd t

he

iden

tifi

ed c

on

sult

atio

ns

and

th

eir

dat

es i

n a

tem

po

rary

sto

rage

No

rma;

T

emp

ora

ry

Sto

rage

Pat

ien

t's

con

sult

atio

ns;

In

itia

l ca

re t

yp

e se

gmen

t's

def

ault

op

enin

g p

erio

d

Co

nsu

ltat

ion

s an

d t

hei

r d

ates

wit

hin

th

e in

itia

l ca

re t

yp

e se

gmen

t's

def

ault

op

enin

g p

erio

d

x

Table 18. Task 2: Deduce Diagnoses Within the Initial Care Type Segment and Create & Validate ECs (Part 1/2)

Su

b-

Sub

task

T

ask

Des

crip

tio

n

Dat

a So

urc

e In

pu

t O

utp

ut

Hu

man

co

gnit

ive

skil

ls

Sub

task

2.1

: D

edu

ce d

iagn

osi

s /

dia

gno

ses

mad

e w

ith

in t

he

init

ial

care

ty

pe

segm

ent'

s d

efau

lt

op

enin

g p

erio

d.

2.1.

1 V

iew

Pat

ien

t's

gen

eral

in

form

atio

n i

n N

orm

a N

orm

a

Pat

ien

t H

isto

ry

(VG

oo

g/V

G

alg/

, Fam

ilie

an

amn

ese)

C

on

clu

sie/

Bel

eid

Su

rger

ies

per

form

ed

Pat

ien

t in

form

atio

n i

n S

ub

ject

Mat

ter

Exp

ert

mem

ory

2.1.

2

Vie

w P

atie

nt'

s in

form

atio

n c

aptu

red

fo

r ea

ch

of

the

con

sult

atio

ns

in N

orm

a th

at a

re w

ith

in

the

init

ial

care

ty

pe

segm

ent'

s d

efau

lt o

pen

ing

per

iod

rec

ord

ed i

n 1

.2.2

Tem

po

rary

st

ora

ge;

No

rma

Co

nsu

ltat

ion

d

ates

; P

atie

nt'

s h

isto

ry

(co

mp

lain

ts),

P

hy

sica

l fi

nd

ings

fo

r ri

ght

and

lef

t ey

e (e

xter

nal

, an

t. &

po

st.

segm

.),

Co

ncl

usi

e/B

elei

d

Pat

ien

t in

form

atio

n i

n S

ub

ject

Mat

ter

Exp

ert

mem

ory

2.1.

3

1) D

edu

ce o

cula

r d

iagn

osi

s fo

r ea

ch c

lin

ical

p

rob

lem

th

e p

atie

nt

suff

ers

fro

m a

cco

rdin

g to

th

e p

atie

nt

info

rmat

ion

vie

wed

in

2.1

.1 &

2.

1.2.

, on

th

e b

asis

of

the

list

of

dia

gno

sis

avai

lab

le i

n t

he

DB

C r

egis

trat

ie

2) R

eco

rd t

he

ded

uce

d d

iagn

ose

s in

a

tem

po

rary

sto

rage

Sub

ject

Mat

ter

Exp

ert

mem

ory

; D

BC

reg

istr

atie

Pat

ien

t in

form

atio

n;

Ocu

lar

dia

gno

ses

Pat

ien

t's

dia

gno

sis

x

Table 19. Task 2: Deduce Diagnoses Within the Initial Care Type Segment and Create & Validate ECs (Part 2/2)

Sub

-Su

bta

sk

Tas

k D

escr

ipti

on

D

ata

Sou

rce

Inp

ut

Ou

tpu

t H

um

an

cogn

itiv

e sk

ills

Sub

task

2.2

: Op

en

EC

fo

r ea

ch

ded

uce

d d

iagn

osi

s in

2.1

an

d v

alid

ate

the

EC

wh

ich

ex

iste

d b

efo

re t

he

vali

dat

ion

pro

cess

.

2.2.

1

a) O

pen

EC

in

th

e D

BC

reg

istr

atie

fo

r ea

ch

dia

gno

sis

reco

rded

in

2.1

.3 w

ith

dia

gno

sis

assi

gned

to

it.

Exc

ept

if t

he

dia

gno

ses

can

be

fou

nd

in

th

e D

iagn

ose

s C

om

bin

atio

n T

able

. b

) F

or

each

EC

wh

ich

is o

pen

ed u

p a

sta

rt d

ate

is a

ssig

ned

to

it,

wh

ich

is

the

dat

e o

f th

e co

nsu

ltat

ion

s w

hen

th

e p

atie

nt

is f

irst

bei

ng

exam

ined

/tre

ated

fo

r th

e E

C's

dia

gno

sis

acco

rdin

g to

th

e p

atie

nt

info

rmat

ion

vie

wed

in

2.

1.2

Tem

po

rary

st

ora

ge;

RSA

D-m

od

el's

ru

les;

Su

bje

ct M

atte

r E

xper

t m

emo

ry

Pat

ien

t's

dia

gno

ses;

D

iagn

osi

s C

om

bin

atio

n

Tab

le;

Pat

ien

t in

form

atio

n

EC

s w

ith

a s

tart

dat

e an

d d

iagn

osi

s as

sign

ed t

o i

t

x

2.2.

2

a) F

ind

th

e d

iagn

osi

s co

de

(DB

C-D

C)

of

the

EC

wh

ich

exi

sted

bef

ore

th

e va

lid

atio

n

pro

cess

, fo

un

d i

n: E

igen

sp

ecia

lism

e -

˃Bo

tto

m

- ˃D

BC

co

de

- ˃D

igit

s 7

to 5

b

) F

ind

th

e D

BC

-DC

of

the

EC

s o

pen

ed u

p i

n

2.2.

1 c)

Val

idat

e if

th

e d

iagn

osi

s o

f th

e E

C w

hic

h

exis

ted

bef

ore

th

e va

lid

atio

n p

roce

ss i

den

tica

l to

on

e o

f th

e d

iagn

ose

s o

f th

e E

Cs

op

ened

up

d

uri

ng

the

vali

dat

ion

pro

cess

in

2.2

.1

DB

C r

egis

trat

ie

DB

C-D

Cs;

DB

C-D

C o

f th

e E

C w

hic

h e

xist

ed b

efo

re t

he

vali

dat

ion

pro

cess

is

iden

tica

l to

on

e o

f th

e D

BC

-D

C o

f th

e E

Cs

op

ened

up

du

rin

g th

e va

lid

atio

n

pro

cess

in

2.1

.4 =

TR

UE

or

FA

LSE

x

2.2.

3

Tw

o a

ctio

ns

po

ssib

le b

ased

on

wh

eth

er t

he

ou

tpu

t fr

om

2.2

.2 is

TR

UE

or

FA

LSE

. See

th

e 2.

2.3

ou

tpu

t fo

r th

e ac

tio

ns.

2.

2.2

Ou

tpu

t

IF T

RU

E:

Del

ete

the

EC

op

ened

up

in

2.1

.4 w

hic

h h

as

iden

tica

l D

BC

-DC

as

the

EC

wh

ich

exi

sted

b

efo

re t

he

vali

dat

ion

pro

cess

E

LSE

: A

lter

th

e in

itia

l E

C's

DB

C-D

C w

hic

h e

xist

ed

bef

ore

th

e va

lid

atio

n p

roce

ss s

o i

t co

rres

po

nd

s to

a

DB

C-D

C f

or

on

e o

f th

e E

Cs

op

ened

up

in

2.1

.4

and

del

ete

that

EC

Table 20. Task 3: Deduce and Link Ocular CAs (Part 1/2)

Sub

-Su

bta

sk

Tas

k D

escr

ipti

on

D

ata

Sou

rce

Inp

ut

Ou

tpu

t H

um

an

cogn

itiv

e sk

ills

Sub

task

3.1

: D

edu

ce a

nd

lin

k

ocu

lar

CA

s to

th

e in

itia

l E

C w

hic

h

exis

ted

bef

ore

th

e va

lid

atio

n p

roce

ss

3.1.

1

a) D

edu

ce o

cula

r su

rgic

al a

nd

dia

gno

stic

CA

s p

erfo

rmed

du

rin

g th

e in

itia

l ca

re t

yp

e se

gmen

t's

def

ault

op

enin

g p

erio

d d

efin

ed i

n

1.2.

1 ac

cord

ing

to t

he

pat

ien

t in

form

atio

n

view

ed i

n 2

.1.1

& 2

.1.2

., o

n t

he

bas

is o

f th

e A

MC

's l

ist

of

ocu

lar

CA

s b

) R

eco

rd t

he

ded

uce

d s

urg

ical

an

d d

iagn

ost

ic

CA

s in

a t

emp

ora

ry s

tora

ge a

lon

g w

ith

th

e d

ates

th

ey w

ere

per

form

ed

1.2.

1;

Sub

ject

Mat

ter

Exp

ert

Mem

ory

; Su

bje

ct M

atte

r E

xper

t M

emo

ry

Init

ial

care

ty

pe

segm

ent'

s d

efau

lt

op

enin

g p

erio

d d

efin

ed

in 1

.2.1

; P

atie

nt

Info

rmat

ion

; A

MC

's l

ist

of

ocu

lar

CA

s (c

on

sult

atio

ns,

su

rgic

al a

nd

dia

gno

stic

C

As)

Su

rgic

al a

nd

dia

gno

stic

CA

s al

on

g w

ith

th

e d

ates

th

ey w

ere

per

form

ed

x

3.1.

2

a) R

emo

ve a

ll o

cula

r C

As

fro

m t

he

AM

C

Zo

rgD

esk

top

wh

ich

are

lin

ked

to

th

e in

itia

l E

C w

hic

h e

xist

ed b

efo

re t

he

vali

dat

ion

pro

cess

b

) A

ssig

n i

n t

he

DB

C r

egis

trat

ie a

ll C

As

ded

uce

d i

n 3

.1.1

an

d t

he

con

sult

atio

ns

iden

tifi

ed i

n 1

.2.2

to

th

e in

itia

l EC

wh

ich

ex

iste

d b

efo

re t

he

vali

dat

ion

pro

cess

T

emp

ora

ry

Sto

rage

Ocu

lar

CA

s al

on

g w

ith

th

e d

ates

th

ey w

ere

per

form

ed r

eco

rded

in

1.

2.2

& 3

.1.1

All

th

e su

rgic

al a

nd

dia

gno

stic

CA

s ar

e li

nk

ed t

o t

he

init

ial E

C w

hic

h e

xist

ed

bef

ore

th

e va

lid

atio

n p

roce

ss

Table 21. Task 3: Deduce and Link Ocular CAs (Part 2/2)

Sub

-Su

bta

sk

Tas

k D

escr

ipti

on

D

ata

Sou

rce

Inp

ut

Ou

tpu

t H

um

an

cogn

itiv

e sk

ills

Sub

task

3.2

: A

ssig

n C

As

to

thei

r ap

pro

pri

ate

EC

3.2.

1

Tw

o a

ctio

ns

po

ssib

le b

ased

on

wh

eth

er

op

ened

ocu

lar

EC

s ar

e 1

or

mo

re. S

ee t

he

3.2.

1 o

utp

ut

for

the

acti

on

s.

DB

C r

egis

trat

ie

IF E

Cs

NU

MB

ER

= 1

C

on

tin

ue

dir

ectl

y t

o t

ask

ste

p 4

.1 (

All

o

cula

r C

As

alre

ady

lin

ked

to

th

e si

ngl

e E

C)

EL

SE

Co

nti

nu

e to

tas

k s

tep

3.2

.2

3.2.

2

Det

erm

ine

for

each

ocu

lar

surg

ical

an

d

dia

gno

stic

CA

to

wh

ich

op

en o

cula

r E

C i

t ap

pli

es t

o &

lin

k i

t to

th

at E

C

DB

C r

egis

trat

ie

Ocu

lar

surg

ical

an

d

dia

gno

stic

CA

s w

hic

h

wer

e li

nk

ed i

n 3

.1.2

to

in

itia

l E

C w

hic

h

exis

ted

bef

ore

th

e va

lid

atio

n p

roce

ss;

Op

ened

ocu

lar

EC

s

Eac

h o

cula

r su

rgic

al a

nd

dia

gno

stic

CA

li

nk

ed t

o t

he

app

rop

riat

e E

C

x

3.2.

3 D

istr

ibu

te e

ven

ly b

etw

een

op

en o

cula

r E

Cs

all

the

con

sult

atio

ns

DB

C r

egis

trat

ie

Ocu

lar

con

sult

atio

ns

wh

ich

wer

e li

nk

ed i

n

3.1.

2 to

in

itia

l EC

w

hic

h e

xist

ed b

efo

re

the

vali

dat

ion

pro

cess

; O

pen

ed o

cula

r E

Cs

C

on

sult

atio

ns

even

ly d

istr

ibu

ted

bet

wee

n

EC

s

Table 22. Task 4: O

pen Additional ECs in Case of Identical Surgical CAs Performed On Both Eyes

Sub

-Su

bta

sk

Tas

k D

escr

ipti

on

D

ata

Sou

rce

Inp

ut

Ou

tpu

t H

um

an

cogn

itiv

e sk

ills

Sub

task

4.1

: O

pen

ad

dit

ion

al

EC

s in

cas

e o

f id

enti

cal s

urg

ical

C

As

per

form

ed

on

bo

th e

yes

4.1.

1

Iden

tify

if

each

of

the

op

en o

cula

r E

C w

hic

h

has

id

enti

cal

ocu

lar

surg

ical

CA

s (e

xclu

din

g su

rgic

al C

As

wh

ich

are

per

form

ed i

n a

sin

gle

op

erat

ion

an

d a

re e

ith

er a

pla

stic

su

rger

y o

f th

e ey

elid

(b

lep

har

op

last

y)

or

a st

rab

ism

us

surg

ery

) p

erfo

rmed

on

bo

th e

yes

D

BC

reg

istr

atie

Op

ened

ocu

lar

EC

s an

d t

he

surg

ical

CA

s li

nk

ed t

o e

ach

of

them

R

esu

lts

rega

rdin

g w

het

her

if

on

e o

r m

ore

of

the

op

en E

Cs

con

tain

ed i

den

tica

l o

cula

r su

rgic

al C

As

x

4.1.

2

Tw

o a

ctio

ns

po

ssib

le b

ased

on

wh

eth

er a

ny

o

pen

EC

s w

ere

fou

nd

in

4.1

.1 w

hic

h c

on

tain

ed

iden

tica

l o

cula

r su

rgic

al C

As.

See

th

e 4.

1.2

ou

tpu

t fo

r th

e ac

tio

ns.

4.

1.1

Ou

tpu

t

IF a

ny

EC

co

nta

ined

id

enti

cal

ocu

lar

care

su

rgic

al

CA

s 1.

Fo

r ea

ch E

C c

on

tain

ing

iden

tica

l o

cula

r su

rgic

al C

As,

op

en a

par

alle

l E

C w

ith

th

e sa

me

dia

gno

sis

and

th

e id

enti

cal s

urg

ical

CA

per

form

ed

late

r li

nk

ed t

o i

t. T

he

op

enin

g d

ay o

f th

e p

aral

lel

EC

is t

he

day

wh

en t

he

latt

er C

A w

as p

erfo

rmed

. 2.

Fin

ish

th

e va

lid

atio

n p

roce

ss

EL

SE

Fin

ish

th

e va

lid

atio

n p

roce

ss

Appendix I: Cognitive Dem

ands Table

Table 23. Cognitive Demands Table (Part 1/3)

Sub

- Su

bta

sk

Des

crip

tio

n

Wh

at t

yp

e o

f er

rors

can

occ

urs

C

ateg

ory

of

pro

ble

m

Cu

es a

nd

str

ateg

ies

to u

se (

stra

tegi

es t

o a

void

th

ese

erro

rs)

1.1.

1

Val

idat

e if

th

e in

itia

l EC

s st

art

dat

e re

cord

ed i

n N

orm

a an

d t

he

DB

C

regi

stra

tie

are

iden

tica

l

Sub

ject

Mat

ter

Exp

ert

can

mis

read

on

e o

r b

oth

of

the

dat

es l

ead

ing

to a

wro

ng

con

clu

sio

n i

n t

he

vali

dat

ion

of

TC

's s

tart

d

ate.

1.

Can

be

auto

mat

ed

wit

ho

ut

hu

man

in

pu

t E

xtra

ct i

nit

ial E

Cs

star

t d

ate

dir

ectl

y f

rom

No

rma

into

DB

C

regi

stra

tie

1.2.

1

Cal

cula

te d

efau

lt i

nit

ial

care

ty

pe

segm

ent'

s en

d d

ate

(= i

nit

ial

care

ty

pe

segm

ent'

s st

art

dat

e +

90

day

s)

1. S

ub

ject

Mat

ter

Exp

ert

can

mis

read

in

itia

l E

C's

sta

rt d

ate

2.

Su

bje

ct M

atte

r E

xper

t ca

n c

alcu

late

wro

ng

def

ault

in

itia

l ca

re t

yp

e se

gmen

t's

end

dat

e 1.

Can

be

auto

mat

ed

wit

ho

ut

hu

man

in

pu

t C

om

bin

e ca

lcu

lati

on

of

the

init

ial

care

ty

pe

segm

ent'

s en

d

dat

e an

d d

efin

ing

the

EC

's s

egm

ents

def

ault

op

enin

g p

erio

d

in s

ingl

e au

tom

ated

pro

cess

ste

p

1.2.

1 D

efin

e th

e in

itia

l car

e ty

pe

segm

ent'

s d

efau

lt o

pen

ing

per

iod

1. S

ub

ject

Mat

ter

Exp

ert

can

mis

read

in

itia

l E

C's

sta

rt d

ate

2.

Su

bje

ct M

atte

r E

xper

t ca

n r

ecal

l w

ron

g va

lid

atio

n p

erio

d e

nd

dat

e 1.

Can

be

auto

mat

ed

wit

ho

ut

hu

man

in

pu

t

1.2.

2

Iden

tify

in

th

e o

verv

iew

of

all

pat

ien

t's

con

sult

atio

n a

nd

th

eir

dat

es i

n N

orm

a ex

cl. p

ho

ne

con

sult

atio

ns

and

rem

ark

s w

hic

h a

re w

ith

in t

he

init

ial c

are

typ

e se

gmen

t's

def

ault

op

enin

g p

erio

d

reco

rded

in

1.2

.1

1. S

ub

ject

Mat

ter

Exp

ert

can

in

corr

ectl

y

reca

ll t

he

init

ial

care

ty

pe

segm

ent'

s d

efau

lt

op

enin

g p

erio

d

2. S

ub

ject

Mat

ter

Exp

ert

can

mis

s ap

pro

pri

ate

con

sult

atio

ns

that

are

in

sid

e th

e in

itia

l ca

re t

yp

e se

gmen

t's

def

ault

op

enin

g p

erio

d

3. S

ub

ject

Mat

ter

Exp

ert

can

id

enti

fy

rem

ark

s an

d p

ho

ne

con

sult

atio

ns

as b

ein

g

1. C

an b

e au

tom

ated

w

ith

ou

t h

um

an i

np

ut

Ext

ract

all

co

nsu

ltat

ion

s d

irec

tly

fro

m N

orm

a in

to D

BC

re

gist

rati

e


Sub

- Su

bta

sk

Des

crip

tio

n

Wh

at t

yp

e o

f er

rors

can

occ

urs

C

ateg

ory

of

pro

ble

m

Cu

es a

nd

str

ateg

ies

to u

se (

stra

tegi

es t

o a

void

th

ese

erro

rs)

2.1.

3

Ded

uce

ocu

lar

dia

gno

sis

for

each

cli

nic

al

pro

ble

m t

he

pat

ien

t su

ffer

s fr

om

acc

ord

ing

to

the

pat

ien

t in

form

atio

n v

iew

ed i

n 2

.1.1

& 2

.1.2

, o

n t

he

bas

is o

f th

e li

st o

f d

iagn

osi

s av

aila

ble

in

th

e D

BC

reg

istr

atie

To

ded

uce

dia

gno

sis/

dia

gno

ses

the

Sub

ject

Mat

ter

Exp

ert

view

s p

atie

nt

info

rmat

ion

sto

red

in

fre

e te

xt. D

ue

to

the

amo

un

t o

f in

form

atio

n t

he

Sub

ject

M

atte

r E

xper

t m

igh

t n

eed

to

in

terp

ret,

th

is t

ask

it

is p

ron

e to

err

ors

as

wro

ng

set

of

dia

gno

ses

can

be

ded

uce

d.

2. C

ann

ot

be

auto

mat

ed

wit

ho

ut

hu

man

in

pu

t

Au

tom

ated

met

ho

d t

o s

up

po

rt t

hat

cli

nic

ian

s o

pen

up

a n

ew E

C

wit

h d

iagn

osi

s as

sign

ed f

or

each

cli

nic

al p

rob

lem

th

at t

he

pat

ien

t su

ffer

s fr

om

at

the

tim

e it

is

dis

cove

red

in

to t

he

DB

C r

egis

trat

ie.

[Co

mm

ent:

th

e m

eth

od

mu

st i

nco

rpo

rate

exe

mp

tio

n r

egar

din

g d

iagn

osi

s fo

un

d i

n t

he

Dia

gno

ses

Co

mb

inat

ion

Tab

le

2.2.

1

Op

en E

C i

n t

he

DB

C r

egis

trat

ie f

or

each

d

iagn

osi

s re

cord

ed i

n 2

.1.3

wit

h d

iagn

osi

s as

sign

ed t

o i

t. E

xcep

t if

th

e d

iagn

ose

s ca

n b

e fo

un

d i

n t

he

Dia

gno

ses

Co

mb

inat

ion

Tab

le.

Sub

ject

Mat

ter

Exp

ert

can

mak

e m

ista

kes

w

hen

ch

eck

ing

for

dia

gno

ses

reco

rded

in

2.

1.3

that

can

be

fou

nd

in

th

e D

iagn

ose

s C

om

bin

atio

n T

able

1. C

an b

e au

tom

ated

w

ith

ou

t h

um

an i

np

ut

2.2.

2

Val

idat

e if

th

e d

iagn

osi

s o

f th

e E

C w

hic

h

exis

ted

bef

ore

th

e va

lid

atio

n p

roce

ss i

den

tica

l to

on

e o

f th

e d

iagn

ose

s o

f th

e E

Cs

op

ened

up

d

uri

ng

the

vali

dat

ion

pro

cess

in

2.2

.1

Sub

ject

Mat

ter

Exp

ert

can

mis

read

on

e o

r b

oth

of

the

dia

gno

sis

lead

ing

to a

wro

ng

con

clu

sio

n i

n t

he

vali

dat

ion

of

the

dia

gno

sis

assi

gned

to

th

e E

C w

hic

h

exis

ted

bef

ore

th

e va

lid

atio

n p

erio

d

1. C

an b

e au

tom

ated

w

ith

ou

t h

um

an i

np

ut

Th

is v

alid

atio

n w

ou

ld b

e n

o l

on

ger

be

nec

essa

ry a

s E

Cs

sho

uld

be

op

ened

up

fo

r al

l cli

nic

al p

rob

lem

s th

at p

atie

nt

suff

ers

fro

m in

2.

1.3-

2.2.

1


Sub

- Su

bta

sk

Des

crip

tio

n

Wh

at t

yp

e o

f er

rors

can

occ

urs

C

ateg

ory

of

pro

ble

m

Cu

es a

nd

str

ateg

ies

to u

se (

stra

tegi

es t

o a

void

th

ese

erro

rs)

3.1.

1

Ded

uce

ocu

lar

surg

ical

an

d d

iagn

ost

ic C

As

per

form

ed d

uri

ng

the

init

ial

care

ty

pe

segm

ent'

s d

efau

lt o

pen

ing

per

iod

def

ined

in

1.

2.1

acco

rdin

g to

th

e p

atie

nt

info

rmat

ion

vi

ewed

in

2.1

.1 &

2.1

.2.,

on

th

e b

asis

of

the

AM

C 's

lis

t o

f o

cula

r C

As

1. S

ub

ject

Mat

ter

Exp

ert

no

t ab

le t

o r

emem

ber

AM

C's

li

st o

f o

cula

r C

As

2. S

ub

ject

Mat

ter

Exp

ert

rem

emb

ers

erro

neo

us

AM

C 's

li

st o

f o

cula

r C

As

3. T

o d

edu

ce o

cula

r C

As

per

form

ed, t

he

Sub

ject

Mat

ter

Exp

ert

view

s p

atie

nt

info

rmat

ion

sto

red

in

fre

e te

xt. D

ue

to t

he

amo

un

t o

f in

form

atio

n t

he

Sub

ject

Mat

ter

Exp

ert

mig

ht

nee

d t

o i

nte

rpre

t, t

his

tas

k is

pro

ne

to e

rro

rs a

s w

ron

g se

t o

f C

As

can

be

ded

uce

d.

2. C

ann

ot

be

auto

mat

ed

wit

ho

ut

hu

man

in

pu

t A

uto

mat

ed m

eth

od

to

su

pp

ort

th

at c

lin

icia

ns

reco

rd a

nd

lin

k o

cula

r su

rgic

al a

nd

dia

gno

stic

C

As

to t

he

app

rop

riat

e E

Cs

at t

he

tim

e th

ey a

re

per

form

ed.

3.2.

2

Det

erm

ine

for

each

ocu

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64 Appendices

Appendix J: Use Case Template Used in the Study

Table 26. Use Case Template Used in the Study

<UC-#> <Title of the use case, describing its goal> <Goal level icon; , , or >

Primary Actor

<Name of the primary actor>

Scope <The system being designed>

Level <Describes the goal level of the use case; summary = , user = , or sub-function = > <Summary goal consists of multiple user goals, showing the context in which they operate>

<User goal is the primary actor's goal when using the system> <Sub-function goal is required to perform a user level goal >

Precondition <What the system guarantees is true before allowing the use case to begin>

Minimal Guarantee

<Specifies what interests of the stakeholders the system can minimally guarantee to be fulfilled after the use case activities are successfully completed.>

Success Guarantee

<Specifies what interests of the stakeholders are fulfilled after the use case activities are successfully completed.>


<The main flow of enumerated action steps describing the activities the system and the stakeholders interacting with it perform during the use case, from trigger to completion.>

Extensions

<Description of under which conditions the action steps take an alternate flow and description of it. It should be noted that when an alternate flow of action steps separates from the main success scenario it can end by merge with the main success scenario again or end within itself.>


<The minimal non-functional requirements (i.e. requirements that express how a system is required to be) that would enables execution of the use case model under design.>

Appendix K: Package 1 “Record Consultation” 65

Appendix K: Package 1 “Record Consultation”

Package 1 “Record Consultation” illustrated in the use case diagram in Figure 15 consisted

of 2 user level use cases; UC-2 (see Table 27) and UC-3 (see Table 28).

Op8t8almologist

DO74S

«uses»UC-2 *ecord

Consultation

UC-3 *ecord Clinical

Data in Consultation

Figure 15. Package 1 “Record Consultation”

Table 27. UC-2: Record Consultation

UC-2 Record Consultation

Primary

Actor

Ophthalmologist

Scope DOTIS

Level User

Precondition Ophthalmologist has DOTIS open and selected a patient that is undergoing a treatment by

him/her which already has been recorded as patient in DOTIS

Minimal

Guarantee

• Ophthalmologist is reminded to record consultation that is taking / took place.

Success

Guarantee

• Ophthalmologist records consultation that is taking / took place.


1. DOTIS reminds Ophthalmologist to record consultation that is taking place or took place earlier and is not

recorded already in the DOTIS.

2. Ophthalmologist records consultation and its date.

3. Ophthalmologist records clinical data in consultation (UC-3).

4. Ophthalmologist confirms that registration is finished.

Extensions

2a. Ophthalmologist does not react on reminder.

2a1. Use case ends.


• All reimbursement data that is recorded must be machine readable and coded according the requirements made to reimbursement datasets that the Grouper should receive.

66 Appendices

Table 28. UC-3: Record Clinical Information in Consultation

UC-3 Record Clinical Information in Consultation

Primary Actor

Ophthalmologist

Scope DOTIS

Level User

Precondition Ophthalmologist has recorded a consultation and diagnosed a patient

Minimal Guarantee

• Ophthalmologist assigns diagnosis to the consultation. • Ophthalmologist is reminded that no CAs are recorded.

Success Guarantee

• Ophthalmologist assigns an appropriate diagnosis to the consultation. • Ophthalmologist records all CAs that were performed during the consultation.


1. DOTIS offers Ophthalmologist to: • Record his observations and patient's complaints. • Record CAs (i.e. ocular CAs, see Appendix D) performed during the consultation on right or left eye. • Assign to the consultation the diagnosis (from a list of ocular diagnoses, see Appendix C) of the clinical

problem which is the main reason for the consultation. 2. Ophthalmologist records clinical data according to his best judgment. 3. DOTIS validates if a diagnosis is assigned to the consultation. 4. DOTIS detects that diagnosis is assigned to the consultation. 5. DOTIS validates if any CAs are recorded. 6. DOTIS detects one or more recorded surgical and other CA.

Extensions

5a. DOTIS detects that no diagnosis is assigned to the consultation. 5a1. DOTIS reports to Ophthalmologist that no diagnosis is assigned to the consultation. 5a2. DOTIS mandates Ophthalmologist to assign diagnosis that applies accurately to the clinical problem

that is the main reason for the consultation. 5a3. Ophthalmologist assigns diagnosis that applies accurately to the clinical problem that is the main

reason for the consultation. 6a. DOTIS detects no surgical or other CA performed on either right or left eye.

6a1. DOTIS reminds Ophthalmologist that no surgical or other CA is recorded for either right or left eye.

6a2. Ophthalmologist records CAs according to his best judgment.


• All CAs recorded must contain information on which day they were performed

Appendix L: Packages 2 and 3 “Link Care Activities to an EC” 67

Appendix L: Packages 2 and 3 “Link Care Activities to an EC”

Package 2 and 3 use cases are presented together in this Appendix as they consist partly of

the same use cases, both made use of package 5, and the goal being accomplished with both

packages was alike. Package 2 is illustrated in the use case diagram in

Figure 16 and Package 3 in Figure 17.

Package 2 consisted of 4 user level use cases; UC-4 (see Table 29), UC-5 (see Table 30), UC-

7 (see Table 32), and UC-10 (see Table 35) as well as 2 sub-function use cases; UC-6 (see

Table 31) and UC-9 (see Table 34). Moreover, it made use of Package 5 described separately

in the following section (see Appendix M).

68 Appendices

Figure 16. Package 2 “Link Consultation and its CAs to an EC”


Package 3 consisted of 2 user level use cases; UC-8 (see Table 33) and UC-10 (see Table 35)

as well as 2 sub-function use cases; UC-6 (see Table 31) and UC-9 (see Table 34).

Furthermore, similar to Package 2 it made use of Package 5 (see Appendix M).

Figure 17. Package 3 “Link CA Recorded with CARS to an EC”

70 Appendices

Table 29. UC-4: Link Consultation and its CAs to an EC

UC-4 Link Consultation and its CAs to an EC

Primary Actor Ophthalmologist

Scope DOTIS

Level User

Precondition Ophthalmologist has already DOTIS open and selected a patient.

Minimal

Guarantee

• Ocular consultation is linked to an EC.

• CAs recorded as having been performed during the consultation are linked to an EC.

• Divide all ECs into segments, which CAs are linked to in UC-4.

Success

Guarantee

• Ocular consultation is linked to an appropriate EC.

• CAs recorded as having been performed during the consultation are linked to an

appropriate EC.

• Divide ECs into segments, which CAs are linked to in UC-4.


1. DOTIS validates if there is any consultation (i.e. ocular consultation) that has not been linked to an EC.

2. DOTIS detects a consultation that has not been linked to an EC and selects it for processing.

3. DOTIS searches for open ECs matching the following search criteria: a) with the same diagnosis linked to it

as the selected consultation, b) the selected consultation is within that EC's opening dates.

4. DOTIS detects one open EC matching the search criteria.

5. DOTIS links the consultation to the EC.

6. DOTIS reports to Ophthalmologist that the consultation has been linked to an EC, with information about

its start date and diagnosis.

7. DOTIS mandates Ophthalmologist to answer YES or NO to whether all the CAs performed during the

consultation and recorded previously in DOTIS apply to the same open EC as the consultation.

8. Ophthalmologist responds to question with YES; DOTIS links all the CAs to the same EC as the

consultation was linked to.

9. DOTIS divides EC into segments (UC-11, part of package 5) which CAs were linked to in the course of

UC-4.

Extensions

2a. DOTIS detects no consultation that has not been linked to an EC.

2a1. Use case ends.

2b. DOT detects two or more consultations that have not been linked to an EC.

2b1. DOTIS selects the oldest consultation for processing.

4a. DOTIS detects no open EC matching the search criteria.

4a1. DOTIS mandates Ophthalmologist to open an EC, which the consultation applies to.

4a2. Ophthalmologist opens an EC to link consultation to (UC-5).

4b. DOTIS detects two or more open EC matching the search criteria.

4b1. DOTIS mandates Ophthalmologist to link consultation to an appropriate EC.

4b2. Ophthalmologist chooses which of the open EC the consultation applies to and should be linked to.

4b2a. Ophthalmologist detects that the consultation does not apply to any of the open EC.

4b2a1. DOTIS mandates Ophthalmologist to open an EC, which the consultation applies to.

4b2a2. Ophthalmologist opens an EC to link consultation to (UC-5).

8a. Ophthalmologist responds to question with NO

8a1. Ophthalmologist links CAs occurring within a consultation to an EC (UC-7).


None


Table 30. UC-5: Open an EC to Link Consultation to

UC-5 Open an EC to Link Consultation to

Primary

Actor

Ophthalmologist

Scope DOTIS

Level User

Precondition DOTIS has mandated the Ophthalmologist to open an EC that the consultation applies to.

Minimal

Guarantee

• EC is opened with diagnosis assigned to it and opening date defined.

Success

Guarantee

• EC is opened with a validated accurate diagnosis assigned to it and opening date defined.


1. Ophthalmologist selects to open a new EC.

2. DOTIS opens an EC with the consultation's date as the opening date and its diagnosis assigned to the EC.

3. DOTIS requests Ophthalmologist to validate whether the diagnosis assigned to the EC is accurate.

4. Ophthalmologist validates the diagnosis and specifies that the diagnosis is accurate.

5. Ophthalmologist confirms that registration of the EC is finished.

6. DOTIS validates the EC (UC-6).

Extensions

4a. Ophthalmologist does not validate the diagnosis assigned to the EC.

4b. Ophthalmologist validates the diagnosis and specifies that the diagnosis is inaccurate.

4b1. DOTIS mandates Ophthalmologist to select accurate diagnosis (from a list of ocular diagnoses, see

Appendix C) for the EC.

4b2. Ophthalmologist selects accurate diagnosis for the EC.


None

72 Appendices

Table 31. UC-6: Validate an EC

UC-6 Validate an EC

Primary

Actor

Ophthalmologist

Scope DOTIS

Level Sub-function

Precondition Ophthalmologist has confirmed that registration of the EC is finished.

Minimal

Guarantee


Success

Guarantee

• The EC is combined to another open EC when their diagnoses exists together in the

Diagnoses Combination Table (see Appendix B)


1. DOTIS validates if the diagnosis assigned to the EC is in the Diagnoses Combination Table.

2. DOTIS does not detect the EC's diagnosis in the Diagnoses Combination Table.

Extensions

2a. DOTIS detects the EC's diagnosis ion the Diagnoses Combination Table.

2a1. DOTIS validates if there are other open ECs with diagnoses existing together with the diagnosis of

the newly opened EC in the Diagnoses Combination Table.

2a2. DOTIS detects no open ECs with diagnoses existing together with the diagnosis of the newly

opened EC in the Diagnoses Combination Table.

2a2a. DOTIS detects an open EC with diagnoses existing together with the diagnosis of the newly

opened EC in the Diagnoses Combination Table.

2a2a1. DOTIS combines the two ECs together.

2a2a2. DOTIS reports to Ophthalmologist that the EC was combined to another EC as their

diagnoses existed together in the Diagnoses Combination Table. Moreover,

specifying which other EC it was combined to and what diagnosis is assigned to that

EC.


None


Table 32. UC-7: Link CAs Occurring within a Consultation to an EC

UC-7 Link CAs Occurring within a Consultation to an EC

Primary

Actor

Ophthalmologist

Scope DOTIS

Level User

Precondition Ophthalmologist has confirmed that not all CAs performed during a consultation and recorded

previously in DOTIS apply to the same open EC as the consultation.

Minimal

Guarantee

• All CAs with their related consultations are linked to an EC

Success

Guarantee

• All CAs with their related consultations are linked to an appropriate EC


1. DOTIS mandates Ophthalmologist to link each CA to an appropriate EC.

2. Ophthalmologist links according to his best judgment each CA to an appropriate open EC.

3. Ophthalmologist confirms that he has finished linking CAs to ECs.

4. DOTIS searches for ECs containing identical surgical CAs performed on both eyes (UC-9).

Extensions

2a. Ophthalmologist detects that one or more CAs do not apply to any of the open ECs.

2a1. DOTIS mandates Ophthalmologist for every CA that does not apply to any of the ECs currently

open, to open an appropriate EC to link CA to (UC-10).

2a2. Ophthalmologist links according to his best judgment each CA to an appropriate open EC.

2a3. DOTIS mandates Ophthalmologist to link consultation to the ECs opened in 2a1 that are related to

them, by moving them from the ECs the consultations were previously linked to.

2a4. Ophthalmologist links according to his best judgment consultations to the ECs opened in 2a1 that

are related to them, by moving them from the ECs the consultations were previously linked to.


None

74 Appendices

Table 33. UC-8: Link CA Recorded with CARS to an EC

UC-8 Link CA Recorded with CARS to an EC

Primary

Actor

CARS

Scope DOTIS

Level User

Precondition CA (i.e. ocular CA, see Appendix D) has been recorded into CARS which has been performed

on an ophthalmology patient and its treating ophthalmologist has DOTIS open

Minimal

Guarantee

• CA performed on a patient for the ophthalmology specialty in a CARS, within the health

care organization are copied to DOTIS and linked to an EC.

• Divide all ECs into segments, which CAs are linked to in UC-8.

Success

Guarantee

• CAs performed on a patient for the ophthalmology specialty in a CARS, within the health

care organization are copied to DOTIS and linked to an appropriate EC.

• Divide ECs into segments, which CAs are linked to in UC-8.


1. CARS reports to DOTIS that a CA (i.e. ocular CA, see Appendix D) has been recorded and sends a copy of

CA to DOTIS.

2. DOTIS receives notification and copy of the CA.

3. DOTIS mandates Ophthalmologist to link CA to an appropriate EC.

4. Ophthalmologist links the CA to an appropriate open EC.

5. Ophthalmologist confirms that he has finished linking CA to an EC.

6. DOTIS searches for ECs containing identical surgical CAs performed on both eyes (UC-9).

7. DOTIS divides EC into segments (UC-11, part of package 5) which the CA was linked to in the course of

UC-8.

Extensions

4a. Ophthalmologist detects that the CA does not apply to any of the open ECs.

4a1. DOTIS mandates Ophthalmologist to open an appropriate EC to link CA to (UC-10).

4a2. Ophthalmologist links the CA to an appropriate open EC

4a3. DOTIS mandates Ophthalmologist to link consultations to the EC opened in 4a1 that are related to

it, by moving them from the ECs the consultations were previously linked to.

4a4. Ophthalmologist links according to his best judgment consultations to the EC opened in 2a1 that are

related to it, by moving them from the ECs the consultations were previously linked to.


None


Table 34. UC-9: Search for ECs Containing Identical Surgical CAs Performed on Both Eyes

UC-9 Search for ECs Containing Identical Surgical CAs Performed on Both Eyes

Primary

Actor

Ophthalmologist

Scope DOTIS

Level Sub-function

Precondition Ophthalmologist has finished linking CAs to ECs and confirmed it.

Minimal

Guarantee

• DOTIS validates whether patient's ECs contain identical surgical CAs performed on both

eyes.

• In the case of EC is found containing identical surgical CAs performed on both eyes, another

EC is opened with diagnosis, opening date, and CAs linked to it.

Success

Guarantee

• DOTIS validates whether patient's ECs contain identical surgical CAs performed on both

eyes.

• In the case of EC is found containing identical CAs performed on both eyes, another EC is

opened with accurate diagnosis, accurate opening date, and appropriate CAs linked to it.


1. DOTIS validates whether any of patient's ECs contain identical surgical CAs performed on both eyes

(excluding plastic surgery of the eyelid (blepharoplasty) or strabismus surgery performed in a single

operation).

2. DOTIS detects no open EC containing identical surgical CAs performed on both eyes.

Extensions

2a. DOTIS detects an EC containing identical surgical CAs performed on both eyes (excluding plastic

surgery of the eyelid (blepharoplasty (CA code in DOT product structure: 31545)) or strabismus surgery

(CA code in DOT product structure: 30941, 30942, 30943, or 30989) performed in a single operation).

2a1. DOTIS opens an EC with the same diagnosis as the EC found.

2a2. DOTIS links one of the two identical surgical CAs to the newly opened EC, by moving it from the

EC that contained the identical CAs.

2a3. DOTIS mandates Ophthalmologist to define opening date of the new EC.

2a4. Ophthalmologist defines the opening date of the new EC

2a5. DOTIS identifies all CAs (i.e. ocular CA, see Appendix D) performed when the two ECs, which

contained the identical surgical CAs intersected.

2a6. DOTIS mandates Ophthalmologist to link each of the CA identified in 2a5 to the appropriate EC of

the two ECs that contained the identical surgical CAs.

2a7. Ophthalmologist links according to his best judgment each CA to the appropriate open EC


None

76 Appendices

Table 35. UC-10: Open an Appropriate EC to Link CA to

UC-10 Open an Appropriate EC to Link CA to

Primary

Actor

Ophthalmologist

Scope DOTIS

Level User

Precondition DOTIS has mandated Ophthalmologist to open an EC to link CA activity with its related

consultation, which does not apply to any of the open ECs.

Minimal

Guarantee

• EC is opened with diagnosis assigned to it and opening date defined.

Success

Guarantee

• EC is opened with both accurate diagnoses assigned to it and opening date defined.


1. Ophthalmologist selects to open a new EC.

2. DOTIS opens a new EC

3. DOTIS mandates Ophthalmologist to define the opening date of the EC accurately.

4. Ophthalmologist defines the opening date of the EC accurately.

5. DOTIS mandates Ophthalmologist to select accurate diagnosis for the EC.

6. Ophthalmologist selects accurate diagnosis for the EC.

7. Ophthalmologist confirms that registration of the EC is finished.

8. DOTIS validates the EC (UC-6).

Extensions

None


None

Appendix M: Package 5 “Divide EC into Segments” 77

Appendix M: Package 5 “Divide EC into Segments”

Package 5 “Divide EC into Segments” illustrated in the use case diagram in Figure 18 which

was used by both Package 2 and 3 (see Appendix L) consisted of 3 sub-function use cases;

UC-11 (see Table 36), UC-12 (see Table 37), and UC-13 (see Table 38).

Figure 18. Package 5 “Divide EC into Segments”

78 Appendices

Table 36. UC-11: Divide EC into Segments

UC-11 Divide EC into Segments

Primary

Actor

Ophthalmologist

Scope DOTIS

Level Sub-function

Precondition Ophthalmologist has finished linking CAs to ECs in UC-4 or UC-8

Minimal

Guarantee


Success

Guarantee

• Divide EC into segments.


1. DOTIS validates whether EC has previously been divided into segments.

2. DOTIS detects that EC has not previously been divided into segments.

3. DOTIS validates whether any surgical CAs are linked to the EC.

4. DOTIS detects no surgical CAs linked to the EC.

5. DOTIS divides EC without surgical CAs into segments (UC-12).

Extensions

2a. DOTIS detects that the EC has previously been divided into segments.

2a1. DOTIS removes all data about how the EC has been divided into segments.

4a. DOTIS detects surgical CAs linked to the EC.

4a1. DOTIS divides EC with surgical CAs into segments (UC-13).

4a2. Use case ends.


None

Appendix M: Package 5 “Divide EC into Segments” 79

Table 37. UC-12: Divide EC without Surgical CAs into Segments

UC-12 Divide EC without Surgical CAs into Segments

Primary

Actor

Ophthalmologist

Scope DOTIS

Level Sub-function

Precondition DOTIS has detected no surgical CAs linked to the EC

Minimal

Guarantee


Success

Guarantee

• Divide EC into segments, which contain no surgical CAs.


1. DOTIS validates if EC has been open 91 days or shorter.

2. DOTIS detects that the EC has been open 91 days or shorter.

3. DOTIS divides the EC into a single initial care type segment. Its opening day is the same day as the EC's

opening date and its closing day is 90 days after the opening date.

Extensions

2a. DOTIS detects that the EC has been open longer than 91 days.

2a1. DOTIS divides the EC into two or more segments. An initial care type segment with the same

opening day as the EC and its closing day is 90 days after the opening date. Day after the closing

day of the initial care type segment, follow-up care type segments are opened up consecutively until

current day is within one of its opening days. Each follow-up care type segment is closed 365 days

after its own opening date.

2a2. Use case ends


None

80 Appendices

Table 38. UC-13: Divide EC with Surgical CAs into Segments

UC-13 Divide EC with Surgical CAs into Segments

Primary

Actor

Ophthalmologist

Scope DOTIS

Level Sub-function

Precondition DOTIS has detected one or more surgical CAs linked to the EC

Minimal

Guarantee


Success

Guarantee

• Divide EC into segments, which contain surgical CAs.


1. DOTIS validates if any of the exemption rules for closing ocular EC segment (see Appendix A) apply.

2. DOTIS detects that none of the exemption rules apply to the EC.

3. DOTIS divides the EC into segments as in divide EC without surgical activities into segments (UC-12)

unless in the segments containing surgical activity. Those segments are closed 42 days after the day the last

surgical activity is performed within them.

Extensions

2a. DOTIS detect that one or more of the exemption rules apply to the EC

2a1. DOTIS divides the EC into segments as in step 3 unless in the segments which the exemption rules

apply. Those segments are divided according to the exemption rules for closing ocular EC segment

(see Appendix A).

2a2. Use case ends.


None

Appendix N: Package 4 “Close ECs and Deduce DTCs” 81

Appendix N: Package 4 “Close ECs and Deduce DTCs”

Package 4 “Close ECs and Deduce DTCs” illustrated in the use case diagram in Figure 19

consisted of 1 user level use case; UC-14 (see Table 39).

9rouper

DO74S

UC-14 Close /Cs

and Deduce D7Cs

Figure 19. Package 4 “Close ECs and Deduce DTCs”

Table 39. UC-14 “Close ECs and Deduce DTCs”

UC-14 Close ECs and Deduce DTCs

Primary Actor

Grouper

Scope DOTIS

Level User

Precondition None

Minimal Guarantee


Success Guarantee

• Close all open ECs of ocular patients according to RSAD-model's rules and deduce DTCs from them.


1. DOTIS searches for open ECs of deceased ophthalmology patients. 2. DOTIS finds no open ECs of deceased ophthalmology patients. 3. DOTIS searches for open ECs of ophthalmology patients in which all segments have been closed and 365

days have passed since their last segments were closed. 4. DOTIS finds one or more ECs fulfilling the criteria in step 3 and closes them. 5. DOTIS sends to Grouper reimbursement dataset for all segments of the ECs found and closed in step 4

and/or 2a1 6. Grouper deduces a DTC from each reimbursement dataset, which it receives. 7. Grouper sends to DOTIS information about the DTCs it deduced for the reimbursement datasets it

received. 8. DOTIS

Extensions

2a. DOTIS finds one or more ECs of ophthalmology patients that are deceased. 2a1. DOTIS closes these ECs.

4a. DOTIS finds no ECs fulfilling the criteria 4a1. Use case ends.


• UC-14 must be executed daily before the Ophthalmology Department's operational hours.

Reimbursements for Diagnosis Treatment Combinations in the Netherlands


Background — In 2005, a prospective payment system was introduced to Dutch

healthcare, the DBC. The system and its successor from 2012 (called DOT) are based on

type of diagnosis-related groups: DTCs (Diagnosis and Treatment Combinations). The

proportion of DTC for which health care organizations were not compensated for cost

overruns increased from 10% of all care provided in 2005 to 70% in 2012. Because of this

change, financial risk for health care organizations has increased in the past few years. To

limit the risk, it is essential that reimbursements accurately reflect the care provided. Our

aim was twofold in the context of the single medical specialty, Ocular Care; first, to

design the functional requirements of an information system that supports accurate

reimbursements determined with the DOT; second, to study the effect on

reimbursements if clinicians were supported by the information system we designed.

Methods — We captured the functional requirements of the information system with a

use case model by first performing requirements elicitation using a set of Cognitive Task

Analysis techniques. To study the change in reimbursements, we conducted a

comparative study of 2 samples of reimbursement data from 108 Ocular Care patients

treated at the Academic Medical Center in Amsterdam. Reimbursement amounts and

their underlying DTCs based on reimbursement data collected with the current practice

for recording reimbursement data (Sample 1) were compared to simulated

reimbursement data if the information system we designed had been used (Sample 2). We

tested the differences in the reimbursement amount between the 2 samples using the

Wilcoxon signed-rank test, with two-tailed p < 0.05 level as the threshold for statistical

significance.

Results — We succeeded in designing a use case model of the information system;

DOTIS. The median reimbursement amount was for Sample 1: EUR 403 and for Sample

2: EUR 422. The statistical test showed that the difference in median reimbursement

amounts was not statistically significant between the samples (p = 0.296), though we

concluded that 22.5% of the patients in our study had different DTCs in both samples.

Conclusion — We recommend further development of DOTIS and the development of

alternative solutions to support accurate DOT reimbursements. Furthermore, we believe

that further research is needed to better draw conclusions about the accuracy of DOT

DTCs and their reimbursements, preferably with a larger sample size. While the median

difference in reimbursement amounts was not significant, the cases where different DTCs

were observed suggests that further research into the accuracy of DOT DTCs is

warranted. Moreover, scientific literature on this topic is scarce.

Keywords — Reimbursements, DOT Prospective Payment System, Ocular Care, System

Design, Comparative Study.

reimbursements for dtcs in the netherlands - supporting their accuracy in ocular care

Documents

thesis possible

ocular care master thesis

academic medical center

accuracy student

limited time

ophthalmology amcuva

research questions

requirement elicitation