Impact Of EHR Design And Operational Procedures On Health Care Data For .

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Impact of EHR design andoperational procedures onhealth care data for researchEdgar Chou, MDChief Value Officer/Chief Medical Informatics OfficerDrexel University College of MedicineSeptember 29, 2016

Objectives Understand the underlying drivers for Electronic Health RecordDesign Understand the drivers that impact data integrity in the ElectronicHealth Record Understand the limitations of data sets

DISCLOSURE STATEMENT None to report

Drexel University College of Medicine: DrexelMedicine Multispecialty ambulatory practice 250 Physicians 17 subspecialties 500 Residents/Fellows 1000 medical students 1000 staff 300,000 outpatient visits per year Implementation of EHR in 2007

How did we get to this point?http://www.hermes-press.com/SP17.jpg, Accessed October 31, 2014

Crossing the Quality Chasm - 2001 6 Aims of Health care: Should be: Safe Effective Patient Centered Timely Efficient Equitable

Crossing the Quality Chasm To be pursued by: Health Care organizationsProfessional GroupsPrivate PurchasersPublic Purchasers Goals: Reduce the burden of illness, injury and disability to improve the health andfunctioning of [our population]

Crossing the Quality Chasm Patient as source of control Shared knowledge and free flow of information Evidence based decision making Safety as a system property Need for transparency Anticipation of needs Decrease in waste Cooperation amongst clinicians

Slides on US healthcare spendingOECD Organization for Economic Co-operation and Development (international organization of 34 countries foundedin 1961 to stimulate economic progress and world trade)

“Once again, U.S. has most expensive, least effective health caresystem in survey” – The Washington Post, June 16, e-least-effective-healthcare-system-in-survey/ , Accessed October 31, 2014

Meaningful UseCenters for Medicare & Medicaid. 2011. Accessed from: lation/EHRIncentivePrograms/Downloads/CQM Webinar 10-25-2011.pdf

Meaningful Use Summaryhttp://www.advisory.com/ s/2012/2014-Edition-MU-Whiteboard 2%2010%2014.pdf

Adoption of Electronic Health /data-brief/2014HospitalAdoptionDataBrief.pdf, last accessed September 13, 2016

Triple Aimà Quadruple Aim “improving the experience of care, improving the health ofpopulations, and reducing per capita costs of health care” – TheInstitute for HealthCare Improvement, 2012Health of theProvider

Quality Payment Program: MeritIncentive Payment System (MIPS)ElementWas WeightFutureWeightReporting MethodQualityPatient Quality ReportingSystem (PQRS)50%30%Registry/QCDR/Web Interface(248 patients)ResourcePhysician Value-PQRS (PV- 10%PQRS)/ Value Modifier (VM)Didn’t exist before15%30%Claims15%Registry/QCDRMeaningful Use (MU)25%Registry/QCDRClinical PracticeImprovementActivity (CPIA)Advancing CareInformation 9/pdf/2016-10032.pdf , last accessed July 12, 2016

Time in the EHR measured For every 1 hour of clinical care delivered, 2 additional hours ofclerical/documentation work in the office An additional 1-2 hours of work occur at home Increased risk for physician burnout Increased risk for lower quality care (and documentation )http://annals.org/article.aspx?articleid 2546704, last accessed September 14, 2016

Medical Education One just needs to look at how physicians are trained in furtherunderstanding the data collected in EHRs “90% of diagnoses come from a detailed history” Identify the reason for visit (Chief Complaint) Discuss the details regarding the complaint (e.g. timing, severity, associated signs andsymptoms) Contributory Family, Medical, Social History, Surgical History Review of Organ Systems to identify other related issues Physical Exam Assessment (synthesis of the information gathered) Plan

Documentation and Billing Complete documentation reflects the performance of the physicianwith the patient and follows a regimented set of guidelines 1995 Evaluation and Management guidelines Chief Complaint (CC)History of Present Illness (HPI)Past Family, Medical, Social HistoryReview of Systems (ROS)Physical ts/Downloadeval mgmt serv guide-ICN006764TextOnly.pdf, Last accessed 8/13/16

Evaluation and Management Guidelines Documentation may either be in HPI or specific sections: html, last accessed 9/6/16

Electronic Progress Notes Benefits: Legible Better quality notes Shortfalls Note Bloat Improper use of “Cut and Paste” with duplication without updating Single Click templates “All normal”

Documentation approaches vary FREE TEXT GOES HERESINGLE CLICK OPTIONS

Data Integrity Data integrity refers to the overall completeness, accuracy andconsistency of data.

Data input: If you’ve seen onephysician you’ve seen one physician Coronary Artery DiseaseCADHeart FailureHeart DiseaseHFCHFCongestive Heart FailureLow Ejection FractionASCVDMyocardial infarctionMIMI s/p stentMI s/p PTCAMI s/p balloon angioplastyCABGCoronary Artery Bypass GraftCHF with EF 55%CHFpEFIschemic Cardiomyopathy

HUH: PACSWEB LINK: URLHUH: HPF:DISCHARGESUMMARIES(Future goal: TaskPCP)IDX (Centricity)Practice Management SystemV5.0Unidirectional to Allscripts:Demographics, Visit Type,Appointment Status, Preferredmethod of communicationCharges fromAllscriptsUnidirectionalConnect RUnidirectionalEnsembleBidirectionalAny EHRQUESTINDEPENDENTLAB VENDORALLSCRIPTS ENTERPRISE V11.2.3BidirectionalHUH: NDEPENDENTLAB VENDORPBARThC o n n e ructrUnidirectional (Future Goal: Bidirectional)Open LinkHUH: SUNQUESTLABSOupatient labresults onlyHUH: IDXRADRADIOLOGYOutpatient radiologyresults onlyHUH: CERNPATHPATHOLOGYInpatient andOutpatient resultsHUH: PBARREGISTRATIONSYSTEM

Most organizations are not like KaiserPermanente Data sources come from disparate sources PayersHospitalsOther health care systemsAncillary Services Home HealthNursing HomesSkilled Nursing FacilitiesLab Vendors

Data Integrity Inaccurate Reporting Sensitivity of reporting ranging from 46-98% per measure1Ann.Internal Med. 2013, 158: 77-83

EHR Data more complete than claims Analysis of diabetics using claims data vs data extracted from the EHR Claims data only identified 75% of existing diabetics EHR data identified 97% of existing diabeticsJ Am Med Inform Assoc. 2007 Jan-Feb;14(1):10-5. Epub 2006 Oct 26

83yo with PMHx CAD, atrial fibrillation, MR s/p MVR,DM, HTN, CHF with EF 45%, urinary incontinencepresents for followup.

83yo with PMHx CAD, atrial fibrillation, MR s/p MVR,DM, HTN, CHF with EF 45%, urinary incontinencepresents for followup.SUMMARY:34 problemsLimit of 10 submissions forMedicare Claims (used to be 4)Diagnoses need to be submittedannually for patients (impacts riskscores)

Documented Plan

Charge Capture

Additional means of data capture

Improving the Health of a Population: Data and PatientQuality Reporting System (PQRS) Case Study: PQRS Measure #7: Beta blocker therapy for Prior Myocardial Infarction or Left VentricularSystolic Dysfunction (LVEF 40%) How would you find this information? PQRS relies on billing codes (ICD-10- formerly ICD-9) Additional background: Left Ventricular Systolic Dysfunction does not have a billing code Requires additional billing code: G8694 Patient Quality Reporting System results reported nationally in 2014 for consumers to allowcomparison with other organizations Value Based Modifier program – Payment to the organization will be based on performanceon quality measures What concerns would you have for your organization? Potential risk for 1) poor outcomes falsely attributed solely based on extraction technique; 2)lower reimbursements

Data and Predictive tent/uploads/2013/05/lemonade.jpg; last accessed ons/f/f9/Poland. Trash 002.JPG; last accessed 9/14/16

What’s .ssf/2014/10/cleveland clinic ibm to begin.html, Accessed November1, 2014

How to improve? Understand the data sources and methods of data entry tounderstand the limitations of the data requested Less focus on programs that do not necessarily improve quality

Additional Sources of Data and FutureApplications Health Share Exchanges Precision Medicine Advancements in Artificial Intelligence

EHR Data more complete than claims Analysis of diabetics using claims data vs data extracted from the EHR Claims data only identified 75% of existing diabetics EHR data identified 97% of existing diabetics J Am Med Inform Assoc. 2007 Jan-Feb;14(1):10-5. Epub 2006 Oct 26

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