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HEOR Trends: Going Broad and Deep in Real-World Evidence

October 2019 | Written by Hiangkiat (Jason) Tan, BPharm, MS

The need for evidence generation for medical decision making has become paramount in recent years, driven in part by technology innovation, a faster regulatory approval process, and rapid changes to the healthcare landscape in the US.

Traditional methods of generating evidence, from single data sources and perspectives, often no longer meet the needs of our complex health system. HealthCore has the data assets and expertise necessary for going broad and deep in real-world evidence generation.

Broad

We have seen growing interest in collaborations between life sciences, large payors, and key opinion leaders in clinical practice [1,2]. Such collaborations ensure that the research questions, study design, and study end points are relevant and meaningful to all stakeholders. The end results are effective dissemination and direct utility in decision making. In addition to many common therapeutic areas, we have also expanded our research to rare diseases and infant populations [3,4].

Deep

Given the fragmentation of health data and the growing complexity of research questions, a single data source is unlikely to be able to provide a “one size fits all” solution. Our research team has conducted multiple studies by integrating administrative claims data with medical chart review and abstraction, patient and physician surveys, laboratory results, genomic results, and patient support programs, among other data sources [5,6]. Each of these data sources provides a unique view and perspective of the healthcare ecosystem. The ability to integrate different data sources enables us to conduct research and answer complex questions effectively. Recent integrations have been carried out without any exchange of protected health information between our organization and external entities. We have been able to achieve this by using innovative matching technology. This has allowed for the connection of different health data with minimal risk of re-identification and without compromising the accuracy of the matching.

[1] Willey V, Franchino-Elder J, Fu AC, Wang C, Sander S, Tan H, Kraft E, Jain R. Treatment and persistence with oral anticoagulants among newly diagnosed patients with non-valvular atrial fibrillation: a retrospective observational study in a US commercially insured and Medicare Advantage population. BMJ Open. 2018 Jun 30;8(6):e020676.
[2] Singhal M, Tan H, Coleman C, Han M, Nguyen C, Ingham M. Effectiveness, treatment durability, and treatment costs of canagliflozin and glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes in the USA. BMJ Open Diabetes Research & Care. 2019 (accepted, in press).
[3] Yu TC, Nguyen C, Ruiz N, Zhou S, Zhang X, Boing EA, Tan H. Prevalence and burden of illness of treated hemolytic neonatal hyperbilirubinemia in a privately insured population in the United States. BMC Pediatr. 2019 Feb 11;19(1):53.
[4] Shieh P, Gu T, Chen E, Punekar R, Tan H. Treatment Patterns and Cost of Care among Patients with Spinal Muscular Atrophy. SMA Researcher Meeting; 2017 June 14– 16, 2017; Orlando, FL.
[5] Visaria J, Thomas N, Gu T, Singer J, Tan H. Understanding the Patient’s Journey in the Diagnosis and Treatment of Multiple Sclerosis in Clinical Practice. Clin Ther. 2018 Jun;40(6):926-939.
[6] Stephenson JJ, Shinde MU, Kwong WJ, Fu AC, Tan H, Weintraub WS. Comparison of claims vs patient-reported adherence measures and associated outcomes among patients with nonvalvular atrial fibrillation using oral anticoagulant therapy. Patient Prefer Adherence. 2018 Jan 12;12:105-117.
AUTHOR(S)
Hiangkiat (Jason) Tan, BPharm, MS
Scientific Director, Health Economics & Outcomes Research
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