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Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions.

ABSTRACT: Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evidence (RWE) obtained from an analysis of real-world data (RWD) from observational studies can bridge gaps in evidence not addressed by RCTs and is thus valuable to public and private payers for decision-making. Through a broad literature search to obtain insights into payers' experience, we found that payers have concerns about real-world studies with respect to data quality, poor internal validity, potential bias, and lack of meaningful endpoints. However, they valued RWE to fill evidence gaps not addressed by RCTs, such as high-quality, real-world, long-term effectiveness and safety data; head-to-head drug comparisons; cost analyses for tiering formulary placement; medication use and adherence patterns; identification of relevant responder and non-responder patient subpopulations; and patient-reported outcomes (PROs). RWE can be used to assess clinically meaningful endpoints and gauge the impact of interventions on the quality of healthcare. Here, we review how payers use or can use RWD on the comparative effectiveness and safety of treatments, PROs, medication adherence and persistence, prescribing patterns, healthcare resource utilization, and patient characteristics and/or biomarkers associated with treatment response when making health technology assessments and payer coverage decisions across therapeutic areas.

SUBMITTER: Roberts MH

PROVIDER: S-EPMC7895868 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions.

Roberts Melissa H MH Ferguson Gary T GT

PharmacoEconomics - open 20210301 1

Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evidence (RWE) obtained from an analysis of real-world data (RWD) from observational studies can bridge gaps in evidence not addressed by RCTs and is thus valuable to public and private payers for decisi ...[more]

PMID: 32557235

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Project description:The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Project description:The use of bone turnover marker (BTM) testing for patients with osteoporosis in the USA has not been well characterized. This retrospective US-based real-world data study found BTM testing has some association with treatment decision-making and lower fracture risk in patients with presumed osteoporosis, supporting its use in clinical practice.IntroductionThe purpose of this study was to characterize bone turnover marker (BTM) testing patterns and estimate their clinical utility in treatment decision-making and fragility fracture risk in patients with osteoporosis using a retrospective claims database.MethodsData from patients aged ≥ 50 years with newly diagnosed osteoporosis enrolled in the Truven MarketScan® Commercial Claims and Encounters and Medicare Supplemental and Co-ordination of Benefits databases from January 2008 to June 2018 were included. Osteoporosis was ascertained by explicit claims, fragility fracture events associated with osteoporosis, or prescribed anti-resorptive or anabolic therapy. BTM-tested patients were 1:1 propensity score matched to those untested following diagnosis. Generalized estimating equation models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for testing versus no testing on both treatment decision-making and fragility fracture.ResultsOf the 457,829 patients with osteoporosis, 6075 were identified with ≥ 1 BTM test following diagnosis; of these patients, 1345 had a unique treatment decision made ≤ 30 days from BTM testing. The percentage of patients receiving BTM tests increased significantly each year (average annual % change: + 8.1%; 95% CI: 5.6-9.0; p = 0.01). Patients tested were significantly more likely to have a treatment decision (OR: 1.14; 95% CI: 1.13-1.15), and testing was associated with lower odds of fracture versus those untested (OR: 0.87; 95% CI: 0.85-0.88).ConclusionIn this large, heterogeneous population of patients with presumed osteoporosis, BTM testing was associated with treatment decision-making, likely leading to fragility fracture reduction following use.

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Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions.

Publications

Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions.

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