rapid response evaluation for physician ofﬁce electronic ...€¦ · emr background literature...

Rapid Response Evaluation for Physician Office

Electronic Medical Records

Morgan Price, MD, PhD, CCFPFrancis Lau, PhD, eHealth Chair

1Tuesday, July 13, 2010

Introductions


eHealth Observatory Mandate

• Monitor effects of Health information system (HIS) deployment

• Build health informatics research capacity

• Engage healthcare community in knowledge translation


Who am I?


Who am I?Lead Researcher at eHealth Observatory



Family Doctor: Cool Aid CHC




UBC Family Medicine Lead Faculty, Informatics





Adjunct Faculty, Comp Sci





Adjunct Faculty, Comp Sci

Informatics Consultant


Thank you to the team• Dr. Francis Lau the eHealth Chair

• eHealth Observatory Team:

• Tyrone Austen

• Jesdeep Bassi

• Heidi Bell

• UBC Enhanced Skills Trainees:

• Dr. Jeanette Boyd

• Dr. James Lai

• Dr. Colin Partridge


Ground Rules

Please Interrupt

Me with Questions!


Activities

• Review Background from the Literature

• Overview of our evaluation framework

• Overview of the methods and tools

• Review Two Case Studies (time permitting)


EMR Background Literature


We are completing a systematic review of

EMR benefits in office practice.


Question:What are the impacts of

EMR systems on physician office practices?


Methods

Main sources MEDLINECINAHL

Supplemental searchesPublication years: 2000-2009CriteriaReviewers


15,847 Papers


15,847 Papers

Analytic Studies22

Descriptive Studies24

Surveys26


72% of survey indicators were for User Satisfaction

System Quality

Information Quality

Service Quality

Use

User Satisfaction

Net Benefits

Quality

Access

Productivity


Analytic Studies


Country # of Studies

% of Total

Netherlands 5 23%

United Kingdom 5 23%

United States 5 23%

Canada 2 9%

Australia 2 9%

New Zealand 2 9%

Italy 1 5%

Total 22 100%

Location of Studies:

50% Europe

32% North America

18% Australia & NZ


90% of measures were focused on Net Benefits

System Quality

Information Quality

Service Quality

Use

User Satisfaction

Net Benefits

Quality

Access

Productivity


Function # of Studies Positive OutcomesPrescribing 7 3

Prevention 4 3CDM 4 2

Test ordering 2 2Recordkeeping 2 0

Other 2 0Referrals 1 1

Total 22 11


Function # of Studies Positive OutcomesPrescribing 7 3

Prevention 4 3CDM 4 2

Test ordering 2 2Recordkeeping 2 0

Other 2 0Referrals 1 1

Total 22 11

Only 2 studies looked at clinical outcomes (no positive findings)


Description of the Descriptive studies

In ProgressIn Progress


There is some evidence of

mixed benefits...lots of room for

improvement


Overview of our Framework


Scanning

LaboratoryResults

DI ReportsImages

Medications

OtherDocuments

ElectronicFeeds In

ElectronicFeeds Out?

Paper Charts

Paper / FaxOut to others

EMR

Other ElectronicRepositories

(Public and Private)

How to Evaluate?


Scanning

LaboratoryResults

DI ReportsImages

Medications

OtherDocuments

ElectronicFeeds In


Paper Charts


EMR


(Public and Private) How to Evaluate?


Scanning

LaboratoryResults

DI ReportsImages

Medications

OtherDocuments

ElectronicFeeds In


Paper Charts


EMR


(Public and Private) How to Evaluate?

How to Provide

Feedback?


We wanted something timely, comprehensible, and useful for clinicians.


We wanted something that could also aid in adoption / impact.


Our framework is built on two sources:

HIMSS & IOM.


HIMSS Analytics


0 Paper Charts for Clinical Information


1

0

Online access to reference materials

Paper Charts for Clinical Information


2

1

0

Hybrid Electronic - Paper Chart




3

2

1

0

Electronic Medical Record Used (more passive record)





4

3

2

1

0

Electronic Chart with Advanced Point of Care Clinical Decision Support






5

4

3

2

1

0

EMR Integrated within Health SystemPoint of Reflection CDSS

Electronic Chart with Advanced Point of Care Clinical Decision Support






Institute of Medicine


IOM created a report on the Key Capabilities

of an EHRS(IOM 2003)


IOM focused on care delivery functions and not

infrastructure / data standards.


IOM - 8 Core Functions


IOM - 8 Core Functions

Health Information & Data

Results Management

Order Entry / Order Management

Decision Support

Electronic Communication

Patient Support

Administrative Processes

Reporting & Population Health


Summarize Adoption as a Graph

0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection


We can show changes over time

0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection


We developed tools to assess adoption


We developed tools to assess adoption

Impact Assessment (I)

EMR Adoption (I)

Workflow Modelling / Rx Functions (I)

Usability Benchmarking (O)

Practice Reflection (G)

EMR System Analysis (I / S)


All resources are available at

ehealth.uvic.ca

(version 2.0 coming)


METHOD: we travel

on site, tools in hand.


Two researchers. Three days

Focus Group

Feedback Focus Group

Interview

Interview

Interview

Interview

Observation

Observation

Interview

Interview

Usability

Usability

Interview

Interview


We provide feedback and recommendations


240 6 12 18

Months

Adoption

Finally, we repeat the study.


Two Case Studies


Case Study 1


Here are the scores.

0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection


Change Management - variable, based on role.


Prescription Management

• Variable use of EMR

• No alerting

• Not using advanced features


Usability

• Multiple errors from system / GUI

• Users did not use tools efficiently

• Drug selection issues (generic / brand)

• Complexity for newer users

• icons not clear

• Complex prescriptions not handled well.


Other Issues

• Participants quick to highlight challenges.

• Many are relative complaints:

• it is slower now

• scanning takes way more mouse clicks than it used to.

• Technical glitches: freezing

• Training issues.


They didn’t complain about what they didn’t know.


Case Study 2(more briefly)


0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection

Here are the scores.


Let’s Compare the Two Cases50Tuesday, July 13, 2010

Case 1

0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection


0

1

2

3

4

5

Health Inform

ation

Order E

ntry

Results M

anagement

Decision S

upport

Electronic C

omm

unication

Patient S

upport

Scheduling B

illing

Practice R

eflection

Case 2


Who implemented when?


Case 2 implemented 10 years before Case 1


Why so little difference?


User Challenges


I know how paper works...


I didn’t know what I didn’t know


Short term fixes have long term implications


Terminology, shmerminology - let me write down what I want.


System Challenges


Group1,000s

Classification10,000s

Reference Terminology100,000s


“Flexible Data models”“Add your own terminologies”


From my office...


How can I see my practice with data like that?


How can we share data in the future?


Materials available atehealth.uvic.ca
