Measuring Population Health Care Performance using EHR data: Are claims data history?

Transcription

1 Measuring Population Health Care Performance using EHR data: Are claims data history? Population Health Science Colloquium September 26, 2013 Matthew Eisenberg MD FAAP

2 Electronic Health Records 2

3 EHR Data Capture: Patient Assessments 3

4 Abstraction of Outside Data into our EHR 4

5 Population Health without informatics is an oxymoron. We need to offer operational informatics skills that allow leaders to obtain the right information for managing change within their healthcare community. We need to offer the data on the epidemiology, the evidence, the outcomes, the prevention, and the safety of care so that leaders can drive more effective and efficient care delivery. We need to focus on how we can operationalize our theory to make a difference. KM Fickenscher, JAMIA July 2013, 20(4):

6 Invest in a Data Infrastructure Metadata: EDW Security and Auditing FINANCIAL SOURCES ( TSI/ EPSI, Lawson) Common, Linkable Vocabulary Others Financial Source Marts Other Source Marts CV- HF Readmissions ADMINISTRATIVE SOURCES (API Time Tracking) Administrative Source Marts Infection Control CLABSI, CAUTI,... Patient Source Marts PATIENT SATISFACTION SOURCES ( Press/Ganey) Population Health EPIC Source Marts Claims Source Mart EMR (Epic) Claims Information 6

7 SHC Clinical & Business Analytics (CBA) Program Teamwork: Drive efforts through multi-functional collaborative teams Leverage the best available science, clinical best practice Embed analytic architects in the clinical/operational team Start small and focus Standard methodology: Aim Scope & Population - Metrics - Analytic tool development - Interventions Continuous Improvement Data flow & Integrity: Map data inputs to outputs Self-service data discovery Agility: Analytical solutions that support evolving user needs and reprioritization 7

8 Primary Care Workgroup Population Health CEC SCC Primary Care PC Workgroup Aim Statement To improve the lives of all of our patients by measuring and improving health outcomes, through data-driven, cost-effective, patient-centered, team and system-based care delivery. ***Focus on the design of a successful delivery model that is scalable and easily diffused PC Workgroup Membership *Dr. Topher Sharp CHAIR Dr. Nancy Morioka-Douglas SFM *Dr. Sang Chang Portola Valley Dr. Baldeep Singh SIM Dr. Kurt Hafer Portola Valley Dr. Joe Hopkins Quality *Dr. Alan Glaseroff SCC SG CHAIR Dr. John Carper UHA/AFP *Dr. Ann Lindsay SCC SG CHAIR Kim Pardini-Kiely Quality Dr. Mark Cullen SOM Dr. Catherine Forest Los Altos Dr. Mike Zimmerman UHA/Quality Kanwar Bhasin CBA Dr. Maia Hightower UHA *Jorge Wilson CBA Dr. Mike McConnell Preventive Cardiology Nancy Lakanen Informatics *Dr. Matt Eisenberg Informatics/CBA Dr. Ron Jimenez Informatics Kai-Ding Zhu CBA John Sheppard CBA Marie Christensen Informatics Deepti Randhava SCC Samantha Valcourt SCC David Rebhan PE Mary Christensen SIM Dr. Bryan Bohman Quality Phillip Morris Portola Valley Dr. Kathan Volrath SCC *Population Health CEC Membership; All subgroup members are part of the workgroup. 8 8

9 Dividing Up the Work (Subgroups) Define our population, panels and responsible provider (PCP) assignment (Empanelment) What do we want to measure first? How will we measure it and how will we validate our measurement? (Data Validation) How do we want to visualize and manipulate the data so we can analyze it and turn data into information that drives improvement and strategy? (Dashboard Design) How will we use these new tools in practice to achieve our aims? (Workflow Design) 9

10 Measure Selection, Definition and Data Validation Select and prioritize outcome and process measures Complete our Data Dictionary definitions and decision making Validate our data definitions Map inputs to outputs and validate dashboard results Coordinate data standardization between our EDW dashboard tools and EPIC electronic health record (Clinical Decision Support) 10

11 Dashboard Design Partner with key stakeholders to understand the data needs Compare industry examples and include business intelligence visualization best practices Translate data needs into the development of an analytics solution (Population Health Dashboard) Identify and approve benchmarks and performance targets Validate final design Specify training requirements for deployment of the tools 11

12 Population Management & Visit Optimization Leveraging Planned Care To Transform Workflow and Culture Deliver Services Optimize Visit Care Follow Up Registry Input & Monitoring Prepare Manage the Population Deliver Services Prepare Follow Up Care Team Building Functional and Clinical Outcomes Reliable, Efficient Workflows 12

13 SCC Population Health Analytics Patient's risk attributed to population health measures are stratified into Green, Yellow, Red based on defined limits of risk by the SCC Patients are then presented in a risk view, allowing providers to sort on patients with the highest risk while easily identifying the measures attributing to the risk 13

14 SCC Population Health Dashboard Summary of overall risk for patient population Panel view by care team, clinician, patient Stratified population risk by outcomes measure Patient classification by total attributable risk 14

15 Between-Visit Panel Review: SCC Speed Dating Monthly care coordination meeting to review shared panel and direct actions based on risk Data analyst generates an excel spreadsheet from the SCC Dashboard, sorted by PCP Update patient status and for each measure formulate & document the following Type of Action Required Action Taken Other Action : used for notes, To Do items, and next steps If no action is taken, rationale is noted 15

16 Primary Care Performance View 16

17 Provider Panel Care Gap Review 17

18 Standard Processes Visit Optimization Team identified that visit could be optimized through session huddles (external set-up) 18 18

19 Visual Management of Visit Optimization Purpose Signals process effectiveness and adherence to standard Red = no; Green = yes Assists with front line problem solving and continuous improvement Functional use Manually edited by care team Posted in highly visible location where care team huddles Expectation is for each team to complete sheet at end of huddle/session 19 19

20 In Search of Joy in Practice CA Sinsky et al, Ann Fam Med 2013;11(3):

21 Conclusions Healthcare is ripe for primary care delivery innovation Designing an AICU and transforming the traditional primary care model isn t easy It can t be done without informatics and analytics applied systematically The EHR is necessary but not sufficient. Large gaps remain Mapping claims data to clinical data capture is not a science EHR data is often incomplete, non-discrete, in disparate systems or just too darn hard to capture (workflow) Health risk stratification predictive analytics is immature or proprietary Even with these new tools, the clinic workflows that got us here, won t get us there If you want to go fast, go alone. If you want to go far, go together! 21

22 Thank you! Matthew Eisenberg MD Christopher Sharp MD 22