Electronic Health Record Impact on Eye Clinic Efficiency: A Time and Revenue Study
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1 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
2 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
3 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
4 Background Healthcare Demands Documentation Evidence-Based Practice Information Exchange Provider Health Plans Patients Technology and Software Development Transforming business, communication, healthcare
5 Background Continued development and implementation is arguably the best potential to improve the delivery, quality, and efficiency of healthcare 1 Institute of Medicine Response EHRs are essential for improving safety, quality, and efficiency of healthcare 2,3
6 Background Adoption and Implantation delays 2008 AAO Survey 4 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 ,6 Financial incentives ($27 billion) for meaningful use Eventual penalties for non-adoption Goal: 85% adoption by healthcare entities over 5 years 2013 AAO Survey 7 32% member adoption 49% user satisfaction 42% Stable productivity 19% decreased or stable costs 31% in the process or intended implementation within 2 years
7 Background Ophthalmologist Concerns 1,3,4,8-11 Medical Error Workflow Limitations Drawing Capabilities Special Testing Costs Efficiency Learning Curve Documentation Quality Chiang MF, et al Clinic Volume 12% after first 3 months 7% after 1 year 3% after 2 and 3 years Documentation Time 6.8 minutes with EHR
8 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
9 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
10 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
11 Study Design Efficiency Study Comparative, prospective, observational study Productivity Study Comparative, retrospective study
12 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
13 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
14 Methods: Efficiency 2 Third-Party Observers Encounter Timing Program Microsoft Access (Redmond, WA) Touch/Click interface Measurements Technician Encounter Times Doctor Encounter Times
15 Methods: Efficiency Encounter Recording Program on Microsoft Access
16 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 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 Technician Time = TDT + TPT Total Doctor Time = DDT + DPT
17 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
18 Timeline Methods: Efficiency Pre-EHR = Paper documentation 4 Months after implementation 18 Months after implementation 4m 18m Pre - EHR Post - EHR Time Study Timeline
19 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
20 Clinic RVUs Methods: Productivity Clinic Encounters and Procedures No Surgical (OR) Encounters Clinic Encounters Clinic Days Worked Accounts for vacations, holidays, OR days
21 Timeline Methods: Productivity Same 4 Consecutive Months at each point November February Comparison of normal fluctuations Vacations (Provider, Patient) Holidays Helps minimize potential errors
22 Timeline Methods: Productivity Pre-EHR = Paper documentation 6 Months after implementation 18 Months after implementation 6m 18m N D J F N D J F N D J F Pre - EHR Post - EHR (N)ovem ber (D)ecem ber (J)anuary (F)ebruary Revenue Study Timeline
23 Methods Primary Outcome Measures Clinic Efficiency (Time Consumption) Total Technician Time Total Doctor Time Clinic Productivity (Revenue Generation) RVUs per Day Worked
24 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
25 Results: Efficiency 871 patient encounters Pre-EHR: 306 4m-EHR: m-EHR: Providers 2 Comprehensive Ophthalmology 1 Glaucoma, Neuro-opthalmology, Oculoplastic 1 Optometrist
26 Results: Efficiency Number of Patient Encounters Pre-EHR 4m-EHR 18m-EHR A B C D E F
27 Time (Minutes) Results: Efficiency 25 Total Technician Time by Encounter Type * Significant * * * * 5 0 Established New Pre-Op Post-Op Paper 4m EHR 18m EHR
28 Time (Minutes) Results: Efficiency 25 Total Technician Time by Provider * Significant * * * * * 5 0 A B C D E F Paper 4m EHR 18m EHR
29 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)
30 Time (Minutes) Results: Efficiency 25 Total Doctor Time by Encounter Type Established New Pre-Op Post-Op Paper 4m EHR 18m EHR
31 Time (Minutes) Results: Efficiency 25 Total Doctor Time by Provider * Significant * * 5 0 A B C D E F Paper 4m EHR 18m EHR
32 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= ) 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)
33 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
34 Encounters Results: Productivity 600 Encounters / Provider A B C D E F Paper 6m EHR 18m EHR
35 Days Results: Productivity Days Worked / Provider * Significant * A B C D E F Paper 6m EHR 18m EHR
36 RVUs Results: Productivity RVUs / Provider * Significant 500 * A B C D E F Paper 6m EHR 18m EHR
37 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
38 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
39 Encounters Results: Productivity Encounters / Day Worked * Significant * A B C D E F Paper 6m EHR 18m EHR
40 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
41 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
42 RVUs Results: Productivity RVUs / Day Worked * * Significant * * A B C D E F Paper 6m EHR 18m EHR
43 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
44 Billing Habits Results: Productivity Coding ability determined by encounter History, Examination, Medical Complexity Would not expect change due to EHR
45 RVU Results: Productivity RVUs / Encounter * Significant * A B C D E F Paper 6m EHR 18m EHR
46 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
47 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
48 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
49 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) per quarter Low Volume Clinic (<100/m) per quarter
50 Overview 1. Background 2. Purpose 3. Design 4. Methods A. Efficiency 5. Results & Discussion A. Efficiency B. Productivity C. Study Comparison 6. Conclusions B. Productivity
51 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
52 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
53 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.
54 References 1. Chiang MF, Boland MV, Brewer A, et al. Special requirements for electronic health record systems in ophthalmology. Ophthalmology 2011 Aug:118(8): Committee on Improving the Patient Record, Division of Health Care Services, Institute of Medicine. The Computer- Based Patient Record: An Essential Technology for Health Care, Revised Edition. Washington, DC: National Academy Press; 1997: Chiang MF, Read-Brown S, Tu DC, et al. Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis). Trans Am Ophthalmol Soc Sep;111: Chiang MF, Boland MV, Margolis JW, et al. Adoption and perceptions of electronic health record systems by ophthalmologists: an American Academy of Ophthalmology survey. Ophthalmology Sep;115(9): Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff (Milwood) 2010;29(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 ): Health Information Technology. Available at: Accessed January 6, 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 Aug; 120: Miller RH, Sim I. Physicians use of electronic medical records: barriers and solutions. Health Aff (Millwood) 2004;23(2): Koppel R, Metlay JP, Cohen A, et al. Role of computer physician order entry systems in facilitating medications errors. JAMA 2005;293(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): 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):
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