How to Use Electronic Medical Record Data to Formulate and Answer Real-World

Questions in Integrated Behavioral Health CareRobyn Shepardson, PhD, Clinical Research Psychologist, VA Center for Integrated HealthcareSuzanne Bailey, PsyD, Director of Integrative Services, Cherokee Health SystemsTawnya Meadows, PhD, Co-Chief of Behavioral Health in Primary Care (Pediatrics), Geisinger Health SystemRola Aamar, PhD, Clinical Effectiveness Consultant, ReliasJennifer Funderburk, PhD, Clinical Research Psychologist, VA Center for Integrated Healthcare

Session # G1

CFHA 20th Annual ConferenceOctober 18-20, 2018 • Rochester, New York

Faculty Disclosure

The presenters of this session have NOT had any relevant financial relationships during the past 12 months.

Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

*No PHI was shared – all EMR screenshots are from fake notes for fake patients

Conference ResourcesSlides and handouts shared in advance by our Conference Presenters are available on the CFHA website at http://www.cfha.net/?page=Resources_2018

Slides and handouts are also available on the mobile app.

Learning ObjectivesAt the conclusion of this session, the participant will be able to:

• Identify variables in the EMR that are relevant to answering IBHC questions

• Describe how to map variables from the EMR to an operationally defined question

• List 3 strategies for obtaining access to EMR data

1. Cifuentes, M., Davis, M., Fernald, D., Gunn, R., Dickinson, P., & Cohen, D. J. (2015). Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. Journal of the American Board of Family Medicine, 28(Suppl. 1), S63-S72.

2. Craner, J. R., Sawchuk, C. N., Mack, J. D., & LeRoy, M. A. (2017). Development and implementation of a psychotherapy tracking database in primary care. Families, Systems, & Health, 35, 207-216.

3. Fondow, M., Schreiter, E. Z., Thomas, C., Grosshans, A., Serrano, N., & Kushner, K. (2017). Initial examination of characteristics of patients who are high utilizers of an established Primary Care Behavioral Health consultation service. Families, Systems, & Health, 35, 184-192.

4. Prusaczyk, B., Fabbre, V., Carpenter, C. R., & Proctor, E. (2018). Measuring the delivery of complex interventions through electronic medical records: Challenges and lessons learned. eGEMs (Generating Evidence & Methods to improve patient outcomes), 6(1): 10.

5. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2, 3.

6. Smith, P. C., Brown Levey, S. M., & Lyon, C. (2017). Evaluating transformation with available resources: The influence of APEX on depression screening. Families, Systems, & Health, 35, 238-247.

Bibliography / References

articles in Families, Systems, & Health special issue with practical strategies about “how we did it”

Learning Assessment

A learning assessment is required for CE credit.

A question and answer period will be conducted at the end of this presentation.

How to Pinpoint a Specific Question

Clinic Size IBHC Required

IBHC Model Specified

Primary Care Coverage

Small No No Not requiredMedium Yes No Per local needLarge Yes Yes - PCBH & CC Per local needVery Large Yes Yes - PCBH & CC Full-timeMedical Centers Yes Yes - PCBH & CC Full-time

VHA mandates for required IBHC services are based on clinic size

Origin of Our Question

Staffing / Coverage

Geographic Catchment Areas

Policy Mandates Hmmm…

ACCESS

Pinpointing Our Question

Does access to IBHC differ by clinic size?

ACCESSIBHC utilization- Overall penetration- Same-day access

How to Identify Relevant Variables in the EMR

Sources of Data in the EMR

• Info collected within IBHC progress notes

• Info that is otherwise available in the EMR

What do clinicians see in the EMR?

Demographics

Demographics

Veteran statusSex

RaceEthnicity

Etc.

What do clinicians see in the EMR?

Progress Notes

Levels of Data

Patient Provider Output

Model Fidelity

Population (SDoH)

Quality Improvement

Identifying Relevant VariablesTREATMENTVARIABLES

• Questionnaire scores

• Type of referral• Referral reason• Presenting

problem• Interventions

used• SMART goals

VISIT-RELATEDVARIABLES

• Visit date• Visit duration• Type of clinic• Clinic location/

site• Provider type

PATIENTVARIABLES

• Demographics• Diagnoses• Other medical

and mental healthcare utilization

• Social determinants of health

BILLINGVARIABLES

• Insurance type

• Relevant billing codes

• Other healthcare visits (ED, Primary Care)

Free-Text Entry

Scaled Items

Pull-Down MenusVS.

Checkboxes

“Pullable” and Easy to Use?

What’s More User-Friendly?

CheckboxesPull-Down MenuFree-Text Entry

What’s More User-Friendly?

Radio ButtonsDiscrete FieldsFree-Text Entry

PHQ-9 item Score (0-3)

AnhedoniaLow moodSleep problemsLow energyAppetite problemsFeel bad about self

Concentration

2

3

1

0

Approaches Using Other EMRs

Data Management Across EMRs

• Creating unique templates can capture exact needed variables

• Access to diagnostic, metabolic, treatment, billing and process data in one place

• Real patient data can support process of funding & grants

• QI initiatives can be monitored using same data as research initiatives

• Can be burdensome process• Too much data analysis paralysis• Most EMRs are not intuitive to MH

treatment and documentation• EMR data reports are rarely clean,

can contain duplicate data, and take time to clean / organize

CHALLENGESBENEFITS

Research Questions & Variables Across EMRs

Comorbidities

Metabolic data

Return for Follow-Up

BillingCodesED Visits

Last PCPVisit

Age, Sex, SES, Race, Language

Appropriate treatmentMedication

Practices

How do high risk patients/ high utilizers respond to treatment differently than other

patients?

Clinician-Led Customization

Customized Behavioral Health Templates

How to Operationalize a Question that Maps on to Available EMR Variables

Does access to IBHC differ by clinic size?

Back to our example:

21

How do we get those variables

out of our EMR data?

Access to IBHC: OverallPenetration Rate

% of the primary carepopulation that

has accessed IBHC

The Easy Way!VA National PC-MHI Dashboard

Service Utilization 2014 2015 2016 2017 2018Unique PatientsEncounters

Individual Group

Average Encounters / UniquePenetration Rate % % % % %VISN LevelSmall Clinic 1Large Clinic 1Large Clinic 2Very Large Clinic 1Medical Center 1

The Hard Way

Penetration Rate

# of primary care patients who meet with an IBHC provider

(stop code 534/539)in a given period of time

total # of primary care patients in the clinic panel

in a given period of time

Access to IBHC: Same-Day

Same-Day Access Rate% of initial IBHC encounters that occur on the same day

that PCP sees / refers the patient Warm Hand-offs

The Easy Way!VA National Same-Day Access Dashboard

National Totals # with same-day PC encounter (numerator)

# new IBHC patients with initial in-person

encounter (denominator)

new IBHC patients with same-day PC

encounter (percentage)

FY2016-presentFY2017 (selected date range)Large ClinicVery Large ClinicMedical CenterVISN LevelLarge Clinic 1Large Clinic 2Very Large Clinic 1Medical Center 1

The Hard Way

Same-Day Access

Rate

# of primary care pts whose initial visitwith IBHC provider (stop code 534)

occurs on the same day as PC appointment (stop code 322/323)

in a given period of time

total # of primary care patients who have initial visits with IBHC provider(stop code 534) in a given period of time

City 1

City 2

City 3

City 4

City 5

Town 1

Town 2

Town 3

VA clinic size classifications

# of unique pts seen annually

Small <1,500 Medium 1,500 – 5,000Large 5,000 – 10,000

Very Large >10,000

Site Clinic Size

Recoded Clinic Size Variable

(S/M/L = 0,VL/MC = 1)

City 1 Medical Center 1City 2 Large 0City 3 Large 0City 4 Very Large 1City 5 Large 0

Town 1 Small 0Town 2 Medium 0Town 3 Medium 0

Site Site Code # of unique pts seen in 2014

City 1 467C6 16,348

City 2 467C2 8,426

City 3 467C1 5,679

City 4 467C3 11,422

City 5 467C5 9,987

Town 1 467C7 1,326

Town 2 467C8 3,476

Town 3 467C4 3,155

Other Variables

National VA Goal = >

• Sex – in EMR (categorical)

• Age – in EMR (continuous)

• Race – in EMR (categorical)

• Diagnoses – had to recode• ICD-10 codes from Problem List

• Identify all codes for relevant MH diagnoses

• Group and recode (0/1) for any depression, PTSD, etc. (0/1)

How to Obtain Access to EMR Data

Obtaining Access to EMR DataStrategies include:

• Partnering with HIT staff

• Working with EMR-savvy administrators who have access to data

• Training research staff to pull data

Obtaining Access to EMR Data• Consider this:

• Do you have a champion that will let you design your own EMR templates?

• Understand capabilities and limitations of your EMR

• Pull data on a regular basis (weekly or monthly) Helps ensure variables are accurate and answer your research question

• Need to find a shared language with all players

• Where else can you find data?• Patient management systems (billing)• Paid claims data

Shared Language, Successful Projects

Gaining Access

Cleaning Data

From Data to Clinical

Insight

Timely and Efficient

Information

Reconciling Disconnected

Systems

Cherokee Health Systems IT Staffing Profile• IT Helpdesk - (4) • EHR Team - (4)

+ Clinical Leadership (Behavioral Health)+ Clinical Leadership (Primary Care)

• Senior Level Infrastructure Support - (4)• Reporting and Software Development - (5)• Clinical Informatics - (1)

EHR Data

Claims Data

Pharmacy Data

HIE Data

Payor Rosters

Patient Data

Data Warehouse

Reporting

Analytics

CHS Data Warehouse

DataMining

CHS Data WarehouseProvides interactive on-demand reporting• Quality metrics • Meaningful Use• UDS• Central Business Office (Financial)• Outreach reports for non-compliant patients• Executive summary dashboard reports• Population Management and Care Coordination

CHS Web Reporter

What Has Worked For Us at CHS? Relationship Building• Clinician on EHR team• Communicate often

Understanding Different Worlds• Clinicians understand IT’s parameters and goals• IT understands clinical flow and goals

Invest in HIT infrastructure• Capture and integrate as much data as you can• Develop real-time reporting capabilities to maximize use of data

How to Prepare Raw EMR Data

for Analysis

How to Prepare Raw EMR Data for Analysis

Considerations:• Need for data cleaning

• How to deal with missing data

• How to deal with discrepant data

• Recoding / combining variables to get needed metrics

Results from ExampleTotal Small / Medium /

Large ClinicsVery Large Clinics /

Medical CentersN unique patients w/ PC visit 61,714 28,815 32,899

N unique patients with IBHC visit 2,783 1,480 1,303Sex (% male) 89.7% 92.0% 87.0%

Age (M) 57.4 57.5 57.2Depression diagnosis 35.7% 36.8% 34.5%PTSD diagnosis 24.3% 29.5% 18.3%Non-PTSD Anxiety diagnosis 20.1% 19.1% 21.2%SUD diagnosis 9.0% 10.7% 7.1%Penetration Rate 4.5% 5.1% 4.0%Telemental health visit in IBHC 33.9% 36.1% 31.4%Same Day Access Rate 25.4% 23.3% 27.9%

Let’s Practice!

Please complete the 2brief session evaluations:

1) Paper form given to you

2) Online form in CFHA Mobile app

Session #: G1Primary Presenter: Shepardson