Characterizing treatment pathways at scale using the OHDSI network

Abstract

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.observational research | data network | treatment pathways A learning health system (1) must systematically evaluate the effects of medical interventions to enable evidence-based medical decision-making. Randomized clinical trials serve as the cornerstone for causal evidence about medical products (2, 3), but evidence from these trials may be limited by an insufficient number of persons exposed, insufficient length of exposure, and inadequate coverage of the target population, factors that limit external generalizability. Observational studies can contribute to the larger goal of causal inference at three stages: (i) the design of experiments, such as determining what are the current therapies that should be compared with a new therapy; (ii) the direct testing of clinical hypotheses on observational data (4-8) using methods to correct for nonrandom treatment assignment as part of the effect estimation process; and (iii) better understanding of population characteristics to improve the extrapolation of both observational and experimental results to new groups.Without sufficiently broad databases available in the first stage, randomized trials are designed without explicit knowledge of actual disease status and treatment practice. Literature reviews are restricted to the population choices of previous investigations, and pilot studi...

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“…10,17 The pathway systems could become a driver of change in clinical practice in the US and around the world, influencing both the patient and provider side of healthcare. In addition, clinical pathways can serve as a framework for moving forward and increasing the efficiency of care while decreasing the variation in care.…”

“…9 A recent study by Hripcsak et al characterized treatment pathways on a global scale and found that the pathways improved consistency of therapy across diseases and locations. 10 In 2012, University of Pittsburgh Medical Center (UPMC) introduced a hysterectomy clinical pathway to reduce the number of total abdominal hysterectomies being performed for benign gynecological indications. 4,11 We hypothesized that pathways could be a unifying vehicle of change for both providers and patients in choosing the most optimal surgical approach to hysterectomy, which can have very important implications for gynecologic care and healthcare in general.…”

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“…For example, Hripcsak et al used OHDSI to combine data from 11 data sources, a total of 250 million patients, to examine treatment pathways in type 2 diabetes mellitus, hypertension and depression [8]. Substantial R&D investments are currently being made to develop tools, platforms and governance processes to enable the distributed analysis of multiple EHR systems [9,10].…”