Representing Knowledge Consistently Across Health Systems

S. T. Rosenbloom; R. J. Carroll; J. L. Warner; M. E. Matheny; J. C. Denny

doi:10.15265/IY-2017-018

Yearbook of Medical Informatics, Inhaltsverzeichnis

Yearb Med Inform 2017; 26(01): 139-147
DOI: 10.15265/IY-2017-018

Section 6: Knowledge Representation and Management

Survey

Georg Thieme Verlag KG Stuttgart

Representing Knowledge Consistently Across Health Systems

S. T. Rosenbloom

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

²Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA

³Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

,

R. J. Carroll

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

,

J. L. Warner

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

³Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

⁴Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA

,

M. E. Matheny

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

³Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

⁵Geriatrics Research Education and Clinical Care, Tennessee Valley Healthcare System VA, Nashville, TN, USA

⁶Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

,

J. C. Denny

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

³Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

› Institutsangaben

Abstract

Summary

Objectives: Electronic health records (EHRs) have increasingly emerged as a powerful source of clinical data that can be leveraged for reuse in research and in modular health apps that integrate into diverse health information technologies. A key challenge to these use cases is representing the knowledge contained within data from different EHR systems in a uniform fashion.

Method: We reviewed several recent studies covering the knowledge representation in the common data models for the Observational Medical Outcomes Partnership (OMOP) and its Observational Health Data Sciences and Informatics program, and the United States Patient Centered Outcomes Research Network (PCORNet). We also reviewed the Health Level 7 Fast Healthcare Interoperability Resource standard supporting app-like programs that can be used across multiple EHR and research systems.

Results: There has been a recent growth in high-impact efforts to support quality-assured and standardized clinical data sharing across different institutions and EHR systems. We focused on three major efforts as part of a larger landscape moving towards shareable, transportable, and computable clinical data.

Conclusion: The growth in approaches to developing common data models to support interoperable knowledge representation portends an increasing availability of high-quality clinical data in support of research. Building on these efforts will allow a future whereby significant portions of the populations in the world may be able to share their data for research.

Keywords

Knowledge representation - Common Data Model - interoperability - Electronic Health Records

Volltext

Referenzen

References
1 Weber GM, Murphy SN, McMurry AJ, Macfadden D, Nigrin DJ, Churchill S. et al. The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories. J Am Med Inform Assoc 2009; 16: 624-30.
2 Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 2013; 15: 761-71.
3 Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N. et al. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med 2011; 03: 79re1.
4 Collins FS, Hudson KL, Briggs JP, Lauer MS. PCORnet: turning a dream into reality. J Am Med Inform Assoc 2014; 21: 576-7.
5 Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc 2014; 21: 578-82.
6 Vogt TM, Lafata JE, Tolsma DD, Greene SM. The Role of Research in Integrated Health Care Systems: The HMO Research Network. Perm J 2004; 08: 10-7.
7 Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using i2b2. J Am Med Inform Assoc 2016; 23: 909-15.
8 Ogunyemi OI, Meeker D, Kim H-E, Ashish N, Farzaneh S, Boxwala A. Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems. Med Care 2013; 51: S45-52.
9 Jiang G, Evans J, Endle CM, Solbrig HR, Chute CG. Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards. J Biomed Semant 2016; 07: 10.
10 Why Hasn’t Big Data Come to the Rescue in Clinical Data Unification? [Internet]. [cited 2017 Apr 7]. Available from: http://www.clinicalinformaticsnews.com/cln/comment/why-big-data-rescue-clinical-data.html
11 Chen PP-S. The Entity-relationship Model—Toward a Unified View of Data. ACM Trans Database Syst 1976; 01: 9-36.
12 Nadkarni PM, Marenco L, Chen R, Skoufos E, Shepherd G, Miller P. Organization of Heterogeneous Scientific Data Using the EAV/CR Representation. J Am Med Inform Assoc 1999; 06: 478-93.
13 Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc 2012; 19: 54-60.
14 FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D. et al. Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 06: 536-47.
15 Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ. et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform 2015; 216: 574-8.
16 ACHILLES for data characterization [Internet]. Obs. Health Data Sci. Inform. 2017 Available from: http://www.ohdsi.org/analytic-tools/achilles-for-data-characterization/
17 Hripcsak G, Ryan PB, Duke JD, Shah NH, Park RW, Huser V. et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci 2016; 113: 7329-36.
18 Yoon D, Ahn EK, Park MY, Cho SY, Ryan P, Schuemie MJ. et al. Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research. Healthc Inform Res 2016; 22: 54-8.
19 Boland MR, Shahn Z, Madigan D, Hripcsak G, Tatonetti NP. Birth month affects lifetime disease risk: a phenome-wide method. J Am Med Inform Assoc 2015; 22: 1042-53.
20 Boyce RD, Ryan PB, Norén GN, Schuemie MJ, Reich C, Duke J. et al. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest. Drug Saf 2014; 37: 557-67.
21 Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc 2011; 18: 441-8.
22 Li Y, Ryan PB, Wei Y, Friedman C. A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions. Drug Saf 2015; 38: 895-908.
23 Voss EA, Boyce RD, Ryan PB, van der Lei J, Rijnbeek PR, Schuemie MJ. Accuracy of an Automated Knowledge Base for Identifying Drug Adverse Reactions. J Biomed Inform [Internet]. [cited 2017 Jan 3]; Available from: http://www.sciencedirect.com/science/article/pii/S1532046416301794
24 Boyce RD, Handler SM, Karp JF, Perera S, Reynolds CF. Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics. eGEMs [Internet]. 2016 [cited 2017 Jan 3];4. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108634/
25 Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus MN. Evaluating common data models for use with a longitudinal community registry. J Biomed Inform 2016; 64: 333-41.
26 Compilation of Patient Protection and Affordable Care Act. Extracted sections concerning Patient-Centered Outcomes Research and the Authorization of the Patient-Centered Outcomes Research Institute (PCORI) [Internet]. pcori.org. 2017 Available from: http://www.pcori.org/sites/default/files/PCORI_Authorizing_Legislation.pdf
27 Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA 2012; 307: 1583-4.
28 Curtis LH, Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP. et al. Design considerations, architecture, and use of the Mini-Sentinel distributed data system. Pharmacoepidemiol. Drug Saf 2012; 21 (Suppl. 01) 23-31.
29 FDA’s Sentinel Initiative [Internet]. [cited 2017 Apr 7]. Available from: https://www.fda.gov/safety/fdassentinelinitiative/ucm2007250.htm
30 PCORnet Common Data Model 3.0 User Guide [Internet]. 2017 Available from: http://pcornet.org/wp-content/uploads/2014/07/PCORnet_CDM_3_Lay_Guide_FINAL.pdf
31 PCORnet Community Rallies to Launch New Version of Common Data Model. pcori.org. 2017
32 Johnston A, Jones WS, Hernandez AF. The ADAPTABLE Trial and Aspirin Dosing in Secondary Prevention for Patients with Coronary Artery Disease. Curr Cardiol Rep 2016; 18: 81.
33 PCORnet Bariatric Study (PBS) [Internet]. ClinicalTrials.gov. 2017 Available from: https://clinicaltrials.gov/ct2/show/NCT02741674
34 Short- and Long-Term Effects of Antibiotics on Childhood Growth (ABX) [Internet]. ClinicalTrials.gov. 2017 Available from: https://clinicaltrials.gov/ct2/show/NCT02744846
35 Blumenthal D, Tavenner M. The “Meaningful Use” Regulation for Electronic Health Records. N Engl J Med 2010; 363: 501-4.
36 Common Clinical Data Set [Internet]. Off Natl Coord Health Inf Technol. 2017 Available from: https://www.healthit.gov/sites/default/files/commonclinicaldataset_ml_11-4-15.pdf
37 D’Amore JD, Mandel JC, Kreda DA, Swain A, Koromia GA, Sundareswaran S. et al. Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc 2014; 21: 1060-8.
38 Bender D, Sartipi K. HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS) 2013; 326-31.
39 HL7 launches Argonaut Project to advance FHIR interoperability standard. Health Manag Technol 2015; 36: 26.
40 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23: 899-908.
41 Mandl KD, Mandel JC, Kohane IS. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst 2015; 01: 8-13.
42 Mandl KD, Kohane IS. Time for a Patient-Driven Health Information Economy?. N Engl J Med 2016; 374: 205-8.
43 Pfiffner PB, Pinyol I, Natter MD, Mandl KD. C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PloS One 2016; 11: e0152722.
44 Wagholikar KB, Mandel JC, Klann JG, Wattanasin N, Mendis M, Chute CG. et al. SMART-on-FHIR implemented over i2b2. J Am Med Inform Assoc. 2016
45 Bloomfield RA, Polo-Wood F, Mandel JC, Mandl KD. Opening the Duke electronic health record to apps: Implementing SMART on FHIR. Int J Med Inform 2017; 99: 1-10.
46 Kasthurirathne SN, Mamlin B, Grieve G, Biondich P. Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System. Stud Health Technol Inform 2015; 216: 932.
47 Kasthurirathne SN, Mamlin B, Kumara H, Grieve G, Biondich P. Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems. J Med Syst 2015; 39: 182.
48 PMI Cohort Program announces new name: the All of Us Research Program [Internet]. 2016 Available from: https://www.nih.gov/allofus-research-program/pmi-cohort-program-announces-new-name-all-us-research-program
49 Alterovitz G, Warner J, Zhang P, Chen Y, Ullman-Cullere M, Kreda D. et al. SMART on FHIR Genomics: facilitating standardized clinico-genomic apps. J Am Med Inform Assoc 2015; 22: 1173-8.
50 Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS. et al. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc 2016; 23: 701-10.
51 Rimer BK. Improving Cancer-Related Outcomes with Connected Health [Internet]. 2016 Nov. Available from: https://deainfo.nci.nih.gov/Advisory/pcp/annualReports/2016/ConnHealth_FullRpt.pdf
52 Kimura E, Ishihara K. Internal domain-specific language based on Arden Syntax and FHIR. Stud Health Technol Inform 2015; 216: 955.
53 Gaebel J, Cypko MA, Lemke HU. Accessing Patient Information for Probabilistic Patient Models Using Existing Standards. Stud Health Technol Inform 2016; 223: 107-12.
54 Lee J, Scott DJ, Villarroel M, Clifford GD, Saeed M, Mark RG. Open-access MIMIC-II database for intensive care research. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 8315-8.
55 Khalilia M, Choi M, Henderson A, Iyengar S, Braunstein M, Sun J. Clinical Predictive Modeling Development and Deployment through FHIR Web Services. AMIA Annu Symp Proc 2015; 2015: 717-26.
56 Doods J, Neuhaus P, Dugas M. Converting ODM Metadata to FHIR Questionnaire Resources. Stud Health Technol Inform 2016; 228: 456-60.
57 Ulrich H, Kock A-K, Duhm-Harbeck P, Habermann JK, Ingenerf J. Metadata Repository for Improved Data Sharing and Reuse Based on HL7 FHIR. Stud Health Technol Inform 2016; 228: 162-6.
58 Hochheiser H, Castine M, Harris D, Savova G, Jacobson RS. An information model for computable cancer phenotypes. BMC Med Inform Decis Mak 2016; 16: 121.
59 Rinner C, Duftschmid G. Bridging the Gap between HL7 CDA and HL7 FHIR: A JSON Based Mapping. Stud Health Technol Inform 2016; 223: 100-6.
60 FHIR Maturity Model [Internet]. HL7 FHIR. 2017 Available from: http://hl7.org/fhir/resource.html#maturity