Perspectives on designing data-driven approaches in healthcare based on real-world evidence
Time: Mon 2025-01-27 13.00
Location: T2 (Jacobssonsalen), Hälsovägen 11C, Huddinge
Video link: https://kth-se.zoom.us/j/63344888971
Language: English
Subject area: Technology and Health
Doctoral student: Luca Marzano , Hälsoinformatik och logistik
Opponent: Associate Professor Guillaume Lamé, Industrial Engineering Department, Cetrale Supélec, Paris
Supervisor: Professor Sebastiaan Meijer, Hälsoinformatik och logistik; Universitetslektor Adam S. Darwich, Hälsoinformatik och logistik; Universitetslektor Jayanth Raghothama, Hälsoinformatik och logistik
QC 2024-12-10
Abstract
According to the United States Food and Drug Administration, real-world data are defined as “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”, and consequently real-world evidence as “the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of the real-world data”. In the context of pharmacology and drug development, real-world data and real-world evidence are gaining interest. The main reason is that these can potentially address the limitations of conventional studies according to best practices of the evidence-based medicine paradigm, from which the prioritization of internal control within experimental settings are often done at the expense of generalization to larger populations treated in routine clinical practice. Instead, from a wider multidisciplinary context, it is relevant to pose questions regarding how real-world data can be used to solve the challenges that health-care systems are facing, such as sensitivity to increasing demand of care, and lack of preparedness to sudden changes and lack of resilience.
Despite the acknowledged potential, real-world data poses a series of challenges due to the purpose for which it is collected, primarily for operational purposes rather than for generating evidence, unlike randomized controlled clinical trials. Healthcare systems have been studied as complex adaptive social systems, where the intricate interactions between sub-systems and actors (e.g., patients and doctors) are challenging to capture in data, often introducing confounders that impact the reliability of data. Therefore, a critical discussion on how to leverage data and the data-driven approaches for generating real-world evidence is essential.
The objective of this thesis is to investigate key elements to account for when designing data-driven approaches aimed to overcome practical challenges of real-world data, and generate real-world evidence. The use of data-driven approaches for analyzing real-world data is studied by exploring how real-world processes are reflected in the collected information, pre-processing the data, selecting the appropriate analytical methods, discussing the model logic in relation to the data, interpreting and generating potential clinical insights based on the outcomes, all with feedback from clinical experts.
The thesis investigates these aspects addressing one central research question, subdivided into three specific research questions, through a multi-case study. The three case studies included in this thesis are: small cell lung cancer treatment, emulation of control arms of clinical trials, and flow management of an overcrowded emergency department and hospital wards. The multi-case study approach facilitated a learning process on a case-by-case basis, contributing valuable insights to each individual case. Subsequently, the patterns in the findings from the case studies were aggregated and harmonized into the general framework for designing data-driven approaches for real-world evidence.
The key aspect of this framework is the position of real-world evidence in an intersection between analytical science (the execution of the data-driven approach), with design science (i.e., the discussion with the clinical experts on data information, modeling design, and generation of new clinical insights) to overcome the limits of pure empirical approaches when encountering practical challenges related to real-world data.
The outcomes suggest that the following key aspects should be accounted for when designing data-driven approaches to achieve real-world evidence in health-care: i) the investigation of discrepancies between the real-world processes and the reported information in the data, ii) verification that information and the designed models lead to results that follow a medical logic, iii) involvement of the clinical experts in all the steps of the analysis, iv) healthcare problems should not be solved using empirical approaches alone, but appropriate approaches can generate relevant insights and inform future studies, v) the main purpose of the analysis should be to provide relevant insights for the improvement of clinical practice, and not be limited to the specific case study, but aim to create a learn-confirm cycle, building on clinical knowledge over time and data readiness for secondary purpose.
In conclusion, we must acknowledge the current limitations of the use of real-world data, but at the same time have a constructive vision on learning about what can be achieved now and what could be achieved in the future, by continuously improving design of data-driven approaches.