Electronic Health Record Impact on Eye Clinic Efficiency: A Time and - - PowerPoint PPT Presentation

Electronic Health Record Impact on Eye Clinic Efficiency: A Time and Revenue Study

Matthew Recko, MD

Derrick Fung, MD, Kyle Smith, MD, Robert H. Rosa, Jr., MD May 16, 2014

Financial Disclosures

• Kyle Smith, MD

– Chief Medical Officer - Integrity Digital Solutions

• No other individual have proprietary or

commercial interest in any of the materials discussed

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Background

• Healthcare Demands

– Documentation – Evidence-Based Practice – Information Exchange

• Provider – Health Plans – Patients
• Technology and Software Development

– Transforming business, communication, healthcare

Background

• Continued development and implementation is

arguably the best potential to improve the delivery, quality, and efficiency of healthcare1

• Institute of Medicine Response

– EHRs are essential for improving safety, quality, and efficiency of healthcare2,3

Background

• Adoption and Implantation delays

– 2008 AAO Survey4

• 12% member adoption

– 69% user satisfaction – 64% stable productivity – 51% stable costs

• 17% in the process or intended implementation within 1 year

– HITECH Act of 20095,6

• Financial incentives ($27 billion) for “meaningful use”
• Eventual penalties for non-adoption
• Goal: 85% adoption by healthcare entities over 5 years

– 2013 AAO Survey7

• 32% member adoption

– 49% user satisfaction – 42% Stable productivity – 19% decreased or stable costs

• 31% in the process or intended implementation within 2 years

Background

• Ophthalmologist Concerns1,3,4,8-11

– Medical Error – Workflow Limitations – Drawing Capabilities – Special Testing

• Chiang MF, et al. 20133

– Clinic Volume

• ↓12% after first 3 months
• ↓7% after 1 year
• ↓3% after 2 and 3 years

– Costs – Efficiency – Learning Curve – Documentation Quality – Documentation Time

• ↑6.8 minutes with EHR

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Purpose

• Impact of Implementing an Eye-Specific EHR

– Clinic Efficiency (Time Consumption)

• Technician Encounter Times
• Provider Encounter Times

– Clinic Productivity (Revenue Generation)

• Relative Value Units (RVUs) Billed
• Encounter Volumes

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Study Design

• Efficiency Study

– Comparative, prospective, observational study

• Productivity Study

– Comparative, retrospective study

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Methods

• Scott & White Eye Institute (Temple, TX)

– Large, academic, multi-specialty group practice

• Integrity EMR for Eye (Belton, TX)

– Certified, Eye-Care Specific, Web-based EHR

• Implementation

– Select providers July 2011 – Full department July 2012

Methods: Efficiency

• 2 Third-Party Observers
• Encounter Timing Program

– Microsoft Access (Redmond, WA) – Touch/Click interface

• Measurements

– Technician Encounter Times – Doctor Encounter Times

Methods: Efficiency

Encounter Recording Program on Microsoft Access

Methods: Efficiency

Total Technician Time

• Documentation Time (TDT)

– Time spent preparing and documenting in patient chart while not in exam room

• Patient Time (TPT)

– Time spent in the exam room

Total Technician Time = TDT + TPT

Total Doctor Time

• Documentation Time (DDT)

– Time spent documenting and completing the patient chart while not in exam room

• Patient Time (DPT)

– Time spent in the exam room

Total Doctor Time = DDT + DPT

Methods: Efficiency

• Tracking Times

– No observer – patient interaction

• One observer tracking multiple encounters

– No loss of data due to irregular patient work-up

• i.e. Visual Field technicians

– No technician times – Doctor times remain valid

– Allows for comparisons among different documentation practices

• Pre-visit Charting, Visit Charting, Post-visit Charting

4m 18m Pre - EHR Post - EHR

Methods: Efficiency

• Timeline

– Pre-EHR = Paper documentation – 4 Months after implementation – 18 Months after implementation

Time Study Timeline

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Methods: Productivity

• Clinic RVUs

– Clinic Encounters and Procedures – No Surgical (OR) Encounters

• Clinic Encounters
• Clinic Days Worked

– Accounts for vacations, holidays, OR days

Methods: Productivity

• Timeline

– Same 4 Consecutive Months at each point

• November – February

– Comparison of normal fluctuations

• Vacations (Provider, Patient)
• Holidays

– Helps minimize potential errors

Methods: Productivity

• Timeline

– Pre-EHR = Paper documentation – 6 Months after implementation – 18 Months after implementation

Revenue Study Timeline N D J F N D J F N D J F

(N)ovember (D)ecember (J)anuary (F)ebruary

6m 18m Pre - EHR Post - EHR

Methods

• Primary Outcome Measures

– Clinic Efficiency (Time Consumption)

• Total Technician Time
• Total Doctor Time

– Clinic Productivity (Revenue Generation)

• RVUs per Day Worked

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Results: Efficiency

• 871 patient encounters

– Pre-EHR: 306 – 4m-EHR: 241 – 18m-EHR: 324

• 6 Providers

– 2 Comprehensive Ophthalmology – 1 Glaucoma, Neuro-opthalmology, Oculoplastic – 1 Optometrist

Results: Efficiency

Pre-EHR 4m-EHR 18m-EHR A 56 50 56 B 48 55 52 C 52 40 53 D 51 16 51 E 43 26 44 F 56 54 68 306 241 324 Number of Patient Encounters

Results: Efficiency

5 10 15 20 25

Established New Pre-Op Post-Op Time (Minutes)

Total Technician Time by Encounter Type

Paper 4m EHR 18m EHR

* * * * * Significant

Results: Efficiency

5 10 15 20 25

A B C D E F Time (Minutes)

Total Technician Time by Provider

Paper 4m EHR 18m EHR

* * * * * * Significant

Discussion: Efficiency

• Total Technician Times

– Overall averages

• Paper – 18.5 minutes
• 4m EHR – 15.7 minutes (-14.9%, p=0.004)
• 18m EHR – 15.9 minutes (-13.8%, p=0.0024)

– No Significant Increases in time for providers or encounter types

• 2 different providers’ technicians had significant decreases in

average times at both time points

– B: -39.6% (4m) and -44.7% (18m) – D: -50.6% (4m) and -49.1% (18m)

Results: Efficiency

5 10 15 20 25

Established New Pre-Op Post-Op Time (Minutes)

Total Doctor Time by Encounter Type

Paper 4m EHR 18m EHR

Results: Efficiency

5 10 15 20 25

A B C D E F Time (Minutes)

Total Doctor Time by Provider

Paper 4m EHR 18m EHR

* * * Significant

Discussion: Efficiency

• Total Doctor Times

– Overall averages

• Paper – 13.1 minutes
• 4m EHR – 10.5 minutes (-19.9%, p=0.0102)
• 18m EHR – 11.5 minutes (-12.8%, p=0.0.0643)

– No Significant Increases in time for providers or encounter types

• 1 provider had significant decreases in average times at both

time points

– E: -50.2% (4m) and -36.1% (18m)

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Results: Productivity

100 200 300 400 500 600

A B C D E F Encounters

Encounters / Provider

Paper 6m EHR 18m EHR

Results: Productivity

2 4 6 8 10 12 14 16 18

A B C D E F Days

Days Worked / Provider

Paper 6m EHR 18m EHR

* * Significant

Results: Productivity

100 200 300 400 500 600 700

A B C D E F RVUs

RVUs / Provider

Paper 6m EHR 18m EHR

* * Significant

Discussion: Productivity

• Basic Productivity Values

– No significant difference in encounter numbers

• Individually or Combined

– Only Provider F had significant changes in days worked (-19.4%) or RVUs (-26.4%)

• Both at 18m
• No significant change of RVUs/Day Worked

Results: Productivity

• Work flow and Volume

– Monthly Encounter impacting variables:

• Frequency of work (OR, Vacation, Holiday)
• Speed of Technicians
• Speed of special testing
• Speed of provider

– Encounters per Day Worked

• Adjusts for frequency of work

Results: Productivity

5 10 15 20 25 30 35 40

A B C D E F Encounters

Encounters / Day Worked

Paper 6m EHR 18m EHR

* * Significant

Results: Productivity

• Encounters per Day Worked

– No significant decreases at 6m or 18m

• Individual Provider or Combined

– One provider had significant increase at 18m

• D: 16.2% increase

Results: Productivity

• Provider Daily Revenue

– Monthly impacting variables:

• Frequency of work (OR, Vacation, Holiday)
• Complexity and Type of Patient encounters

– RVUs per Day Worked

• Adjusts for frequency of work

Results: Productivity

5 10 15 20 25 30 35 40 45 50

A B C D E F RVUs

RVUs / Day Worked

Paper 6m EHR 18m EHR

* * * * Significant

Results: Productivity

• RVUs per Day Worked

– No significant decreases at 6m or 18m

• Individual Provider or Combined

– 3 providers had significant increases at 18m

• C: 12.7% increase
• D: 27.8% increase
• E: 22.5% increase

Results: Productivity

• Billing Habits

– Coding ability determined by encounter

• History, Examination, Medical Complexity

– Would not expect change due to EHR

Results: Productivity

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

A B C D E F RVU

RVUs / Encounter

Paper 6m EHR 18m EHR

* * Significant

Results: Productivity

• RVUs per Encounter

– No significant decreases at 6m or 18m

• Individual Provider or Combined

– One provider had significant increase at 18m

• E: 25% increase

– Changes

• Possible Coding Engine Coaching

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Study Comparison

• Chiang MF, et al.3

– Documentation Time Methods:

• Measured time encounter opened until closed
• Small comparison for providers using both

– Self logged times

– Results:

• EHR averaged 6.8 minutes longer (p<0.01)
• Range: Minutes to Weeks

Study Comparison

• Chiang MF, et al.3

– Volume Methods:

• 3 months prior (paper) v 3 years after EHR

– Results:

• 12% Reduction at 3 months
• 7% Reduction at 1 year
• 3% Reduction at year 2 and 3
• High Volume Clinic (>100/m) - ↑6.7 per quarter
• Low Volume Clinic (<100/m) - ↓3.7 per quarter

Overview

• 1. Background
• 2. Purpose
• 3. Design
• 4. Methods
• A. Efficiency
• B. Productivity
• 5. Results & Discussion
• A. Efficiency
• B. Productivity
• C. Study Comparison
• 6. Conclusions

Conclusions

• EHRs are becoming standard part of medicine
• Implementation Incentives and potential penalties

for lack of implementation

• Many provider concerns for possible negative

impact of EHRs

• Lack of research on EHRs in ophthalmology

– Even fewer looking at impact on clinics

Conclusions

• Our Study:

– No significant decrease in efficiency or productivity with implementation of our EHR

• Individual user dependent

– Provides practical assessment for EHR impact:

• Technicians and Providers Encounter times
• Daily clinic revenue changes
• Possible modifications of billing practices

Conclusions

• We hope that our paper presents valid

measures to assess the true impact of EHR implementation of clinic efficiency and to encourage future studies which objectively and accurately evaluate the impact of electronic health records on clinical practice.

References

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• phthalmologists: an American Academy of Ophthalmology survey. Ophthalmology. 2008 Sep;115(9):1591-7

5. Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff (Milwood) 2010;29(6):1214-1219. 6. Congressional Budget Office. Estimated effect on direct spending and revenues of Title IV of Division B of the American Recovery and Reinvestment Act of 2009 (Public Law 111-15): Health Information Technology. Available at: http://www.cbo.gov/ftpdoccs/101xx/doc10106/health1.pdf. Accessed January 6, 2011. 7. Boland MV, Chiang MF, Lim MC, et al. Adoption of electronic health records and preparations for demonstrating meaningful use: an American Academy of Ophthalology survey. Ophthalmology. 2013 Aug; 120:1702-1710 8. Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff (Millwood) 2004;23(2):116-126 9. Koppel R, Metlay JP, Cohen A, et al. Role of computer physician order entry systems in facilitating medications errors. JAMA 2005;293(10):1197-1203. 10. Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after implantation of a commercially sold computerized physician order entry system. Pediatrics 2005;116(6):1506-1512. 11. Miller RH, Sim I, Newman J. Electronic medical records in solo/small groups: a qualitative study of physician user types. Stud Health Technol Inform 2004;107(Pt 1):658-662.