How To Improve Data Collection

Transcription

1 Integration with EMR for Local Databases Roberto A. Rocha, MD, PhD, FACMI Sr. Corporate Manager Clinical Knowledge Management and Decision Support, Partners ecare, Partners Healthcare System Lecturer in Medicine Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women s Hospital, Harvard Medical School Workshop on Transplant and Cellular Therapy for Autoimmune Diseases April 19 30, 2013

2 Topics Data collection for clinical care and research purposes using existing EMR systems (commercially available) Extraction and repurposing of data from EMR systems to research data repositories (registries)

3 Clinical System an automated system with a long term database containing clinical information used for patient care. Bruce Blum, 1986 Support (automation) for one or multiple clinical (patient care) functions Electronic Health/Medical Record system is an integrated suite of clinical systems

4 Separate clinical systems EHRs/EMRs: Electronic Health/Medical Record Systems Medical Record Systems Clinical Research Systems PHRs: Patient Health Records

5 Known consequences Medical Record Systems Manual extraction Data re entry Missing data Misinterpretations Limited contextual details Disparate data definitions Laborious data extraction Overlapping standards Clinical Research Systems

6 New data management framework Distributed data collection Combine data from multiple sources Rely on EMRs for point of care (bedside) data Simplify data extraction and integration Minimize data transformation and re mapping Ensure preservation of meaning and context On demand data aggregation Minimize transfers (federated data management) Ability to support evolving/new perspectives

7 EMRs: data collection Sophisticated data gathering features E.g., forms, flowsheets, structured text, etc. Extensible data representation (storage) Typically unlimited number of data points (variables) Important incentives to adopt standards Steady progress towards semantic interoperability o Collecting structured data is time consuming o Changes to configured forms have side effects o Difficult to ensure data consistency and quality

8 EMRs: document standardization HL7 Clinical Document Architecture Release 2 (CDA R2) Specifies the structure and semantics of clinical documents for the purpose of exchange Meaningful Use (Stages 1 & 2) Dolin RH, Alschuler L, Beebe C, Biron PV, Boyer SL, Essin D, Kimber E, Lincoln T, Mattison JE. The HL7 Clinical Document Architecture. J Am Med Inform Assoc Nov Dec;8(6):

9 EMRs: data standardization Birth Weight: <number><units> LOINC : Body weight^at birth Mass; Pt; ^Patient; Qn; Measured Data Element: numeric measurement with unit Topic Value set Standard data definitions: forms/templates, data elements, and data values SNOMED CT (or UCUM) : Units of mass (SI) SNOMED CT : gram, g Value (concept)

10 HL7 Health Level Seven (Healthcare IT Standards)

11 ASCO & HL7 effort Clinical Oncology Treatment Plan and Summary document Draft standard: 204 pages (specification) form will promote interoperability; create information suitable for reuse in quality measurement, public health, research, and for reimbursement. (Example)

12 2009 American Society of Clinical Oncology. All rights reserved American Society of Clinical Oncology s Breast Cancer Adjuvant Treatment Plan and Summary

13 Example: Multiple Sclerosis Pre HSCT Disease Assessment at Diagnosis CDA Documentlevel templates Treatment for Multiple Sclerosis α interferon treatment β interferon treatment Anti lymphocyte antibodies treatment treatment stopped reason treatment stopped Multiple Sclerosis Pre HSCT Data Section level templates Entry level templates All point of care (bedside) data? Clinical care workflow? Pre Mobilization Evaluation Date of evaluation Scripps neurological rating scale Kurtze functional systems scale Pyramidal Scale score Cerebellar Scale score

14 Standardized data transmission EMR to cancer registry Single standardized method (HL7 CDA) Efficient and accurate data transmission Decreases burden on EMR system specific or registry specific implementations

15 Incentives for advanced EMRs Meaningful Use Stages More sophisticated clinical systems, requiring an ever increasing variety (and amount) of structured and coded data

16 Evolution of EMRs (in progress) New generation of systems beyond efficient record storage and communication New paradigm with pervasive computerized data analysis and decision support Widespread use of interoperable services and data, with advanced functions that enable team based care

17 Data Management with EMR standards EMRs with CDA compliant features become the enablers for an integrated and continuous research data management framework Uniform data representation using CDA templates Standard specifications and highly re usable (modular) Meaning and context are explicitly represented Uniform process to retrieve and process data from any CDA compliant EMR (documents or messages) o Effort to develop (modeling) templates: researchers o Effort to implement & deploy templates (EMRs)

18 Clinical research workflow Kahn MG, Weng C. Clinical research informatics: a conceptual perspective. J Am Med Inform Assoc Jun;19(e1):e36 42.

19 Thank you! Roberto A. Rocha, MD, PhD