Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data

Summary

Background: Creation of a new electronic health record (EHR)-based registry often can be a “one-off” complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care.

Objective: To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development.

Methods: We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed - either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) - were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined “grains” from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-gener-ated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week “sprints” for rapid-cycle feedback and refinement.

Results: Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends.

Conclusions: This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.

Keywords

Registries - electronic health records - data collection - outcome and process assessment (health care), - quality indicators - information storage and retrieval - data warehouse - agile development - population health

 

• References

• 1 Porter ME. What is Value in Health Care?. N Engl J Med 2010; 363 (Suppl. 26) 2477-2481.
• 2 Gliklich RE, Dreyer NA. Registries for Evaluating Patient Outcomes: A User’s Guide. 3rd ed. Rock-ville, MD: Agency for Healthcare Research and Quality;; 2014
• 3 Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for High-Need, High-Cost Patients - An Urgent Priority. N Engl J Med 2016; 375 (Suppl. 10) 909-911.
• 4 Pruitt S, Annandale S, Epping-Jordan J, Fernandez Diaz JM, Khan M, Kisa A, Klapow J, Solinis RN, Reddy S, Wagner E. Innovative Care for Chronic Conditions: Building Blocks for Action. Geneva: World Health Organization;; 2002
• 5 Larsson S, Lawyer P, Garellick G, Lindahl B, Lund-strom M. Use Of 13 Disease Registries In 5 Countries Demonstrates The Potential To Use Outcome Data To Improve Health Care’s Value. Health Affairs 2011; 31 (Suppl. 01) 220-227.
• 6 Backus LI, Gavrilov S, Loomis TP, Halloran JP, Phillips BR, Belperio PS. et al. Clinical Case Registries: Simultaneous Local and National Disease Registries for Population Quality Management. JAMIA 2009; 16 (Suppl. 06) 775-783.
• 7 Emilsson L, Lindahl B, Köster M, Lambe M, Ludvigsson JE. Review of 103 Swedish Healthcare Quality Registries. J Intern Med 2014; 277 (Suppl. 01) 94-136.
• 8 Wright A, McGlinchey EA, Poon EG, Jenter CA, Bates DW, Simon SR. Ability to Generate Patient Registries Among Practices With and Without Electronic Health Records. Journal of Medical Internet Research 2009; 11 (Suppl. 03) e31.
• 9 Benkert R, Dennehy P, White J, Hamilton A, Tanner C, Pohl J. Diabetes and Hypertension Quality Measurement in Four Safety-Net Sites. Appl Clin Inform 2014; 5 (Suppl. 03) 757-772.
• 10 Krauss JC, Boonstra PS, Vantsevich AV, Friedman CP. Is the problem list in the eye of the beholder? An exploration of consistency across physicians. JAMIA 2016; 23 (Suppl. 05) 859-865.
• 11 Cusack CM, Hripcsak G, Bloomrosen M, Rosen-bloom ST, Weaver CA, Wright A. et al. The future state of clinical data capture and documentation: a report from AMIA’s 2011 Policy Meeting. JAMIA 2013; 20 (Suppl. 01) 134-140.
• 12 Kimball R, Ross M. The Data Warehouse Toolkit: the Definitive Guide to Dimensional Modeling. Indianapolis: Wiley;; 2013
• 13 Collier K. Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing. Pearson; 2012
• 14 Hughes R. Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum. Elsevier (Morgan Kaufmann); 2013
• 15 Rubin KS. Essential Scrum: a Practical Guide to the Most Popular Agile Process. Upper Saddle River, NJ: Addison-Wesley;; 2012
• 16 Larman C. Agile and Iterative Development: A Manager’s Guide. Upper Saddle River, NJ: Addis-on-Wesley; 2004
• 17 Greenberg AE, Hays H, Castel AD, Subramanian T, Happ LP, Jaurretche M. et al. Development of a large urban longitudinal HIV clinical cohort using a web-based platform to merge electronically and manually abstracted data from disparate medical record systems: technical challenges and innovative solutions. JAMIA 2016; 23 (Suppl. 03) 635-643.
• 18 Cohn M. User Stories Applied: For Agile Software Development. Upper Saddle River, NJ: Addison-Wesley;; 2004
• 19 Ambler SW. Agile modeling: effective practices for eXtreme programming and the unified process. New York: Wiley;; 2002
• 20 Rosenberg D, Stephens M. Use Case Driven Object Modeling with UML: Theory and Practice. New York: Apress;; 2013
• 21 Wright A, McCoy AB, Hickman T-TT, Hilaire DS, Borbolla D, Bowes WA. et al. Problem list completeness in electronic health records: A multi-site study and assessment of success factors. International Journal of Medical Informatics 2015; 84 (Suppl. 10) 784-790.
• 22 Ainsworth J, Buchan I. Combining Health Data Uses to Ignite Health System Learning. Methods Inf Med 2015; 54 (Suppl. 06) 479-487.