Sebastian Schneeweiss1, William H Shrank2, Michael Ruhl3, Malcolm Maclure4. 1. Division of Pharmacoepidemiology and Pharmacoeconomics,Department of Medicine,Brigham and Women's Hospital and Harvard Medical Schoolschneeweiss@post.harvard.edu. 2. CVS Health,Woonsocket,Rhode Island. 3. Aetion Inc. 4. Department of Anesthesiology,Pharmacology and Therapeutics,University of British Columbia.
Abstract
BACKGROUND: Availability of real-time electronic healthcare data provides new opportunities for rapid-cycle evaluation (RCE) of health technologies, including healthcare delivery and payment programs. We aim to align decision-making processes with stages of RCE to optimize the usefulness and impact of rapid results. Rational decisions about program adoption depend on program effect size in relation to externalities, including implementation cost, sustainability, and likelihood of broad adoption. METHODS: Drawing on case studies and experience from drug safety monitoring, we examine how decision makers have used scientific evidence on complex interventions in the past. We clarify how RCE alters the nature of policy decisions; develop the RAPID framework for synchronizing decision-maker activities with stages of RCE; and provide guidelines on evidence thresholds for incremental decision-making. RESULTS: In contrast to traditional evaluations, RCE provides early evidence on effectiveness and facilitates a stepped approach to decision making in expectation of future regularly updated evidence. RCE allows for identification of trends in adjusted effect size. It supports adapting a program in midstream in response to interim findings, or adapting the evaluation strategy to identify true improvements earlier. The 5-step RAPID approach that utilizes the cumulating evidence of program effectiveness over time could increase policy-makers' confidence in expediting decisions. CONCLUSIONS: RCE enables a step-wise approach to HTA decision-making, based on gradually emerging evidence, reducing delays in decision-making processes after traditional one-time evaluations.
BACKGROUND: Availability of real-time electronic healthcare data provides new opportunities for rapid-cycle evaluation (RCE) of health technologies, including healthcare delivery and payment programs. We aim to align decision-making processes with stages of RCE to optimize the usefulness and impact of rapid results. Rational decisions about program adoption depend on program effect size in relation to externalities, including implementation cost, sustainability, and likelihood of broad adoption. METHODS: Drawing on case studies and experience from drug safety monitoring, we examine how decision makers have used scientific evidence on complex interventions in the past. We clarify how RCE alters the nature of policy decisions; develop the RAPID framework for synchronizing decision-maker activities with stages of RCE; and provide guidelines on evidence thresholds for incremental decision-making. RESULTS: In contrast to traditional evaluations, RCE provides early evidence on effectiveness and facilitates a stepped approach to decision making in expectation of future regularly updated evidence. RCE allows for identification of trends in adjusted effect size. It supports adapting a program in midstream in response to interim findings, or adapting the evaluation strategy to identify true improvements earlier. The 5-step RAPID approach that utilizes the cumulating evidence of program effectiveness over time could increase policy-makers' confidence in expediting decisions. CONCLUSIONS: RCE enables a step-wise approach to HTA decision-making, based on gradually emerging evidence, reducing delays in decision-making processes after traditional one-time evaluations.
Entities:
Keywords:
Decision making; Healthcare data analytics; Rapid-cycle evaluation
Authors: Amanda Sandbæk; Line Due Christensen; Lotte Lykke Larsen; Nina Primholdt Christensen; Frida Greek Kofod; Ann Dorrit Guassora; Camilla Hoffmann Merrild; Elisabeth Assing Hvidt Journal: JMIR Form Res Date: 2021-11-24
Authors: Shirley V Wang; Sebastian Schneeweiss; Marc L Berger; Jeffrey Brown; Frank de Vries; Ian Douglas; Joshua J Gagne; Rosa Gini; Olaf Klungel; C Daniel Mullins; Michael D Nguyen; Jeremy A Rassen; Liam Smeeth; Miriam Sturkenboom Journal: Pharmacoepidemiol Drug Saf Date: 2017-09 Impact factor: 2.890