Interview: Mark Weiner, Temple University Health System on Maturity Assessment of Healthcare Analytics
We discuss the challenges and opportunities created by increased collection of healthcare data, state of data accessibility, and the value of Analytics to the drug development process.

Throughout his career, he has conducted research and applied techniques that help bridge the gap between Health Services Research, clinical and research operations and Medical Informatics.
Here is my interview with him:
Anmol Rajpurohit: Q1. How has the recent increase in digitization and analysis of healthcare data impacted the drug development process? What are the top challenges and opportunities?

Given the benefits of blockbuster drugs from years past, it is harder to develop a new drug that can replace these and capture a similar market share. The trick is finding the population that is not achieving the expected benefit, and develop new medicines that will work better for them.
AR: Q2. Data access has generally been one of the top challenges across healthcare analytics. Can you elaborate on the data desired by pharmaceutical industry and how readily that data is accessible?

Even when these data are available, the data collection is often captured when patients are not feeling well, and seeing the provider for a specific reason. With few exceptions like blood pressure and blood sugar monitoring, it is hard to capture data on regular intervals regardless of how the patient is feeling at that moment – and even there, the rigor and regularity of the data collection may differ widely for different patients. It is important to account for the significance of the variability in availability of data when conducting and interpreting an analysis.
For example, if a patient has a pattern of routine labs once every year, but then has a series of 4 sets of routine labs in a 1 month period, the fact that there was a change in pattern to the labs is clinically relevant, and may impact expected outcomes, even if the laboratory results themselves were normal.

A good deal of research can be accomplished with de-identified data, but, by design, there is no way to follow up with patients. It is also difficult to merge de-identified data on the same patient across different institutions, though there has been some work on matching one-way hashed transformations of the original identifiable data to facilitate cross-institutional matching while still maintaining patient privacy.
AR: Q3. What measures would you recommend to address the data accessibility challenges?

True data interoperability across different Electronic Health Records at different institutions is not as common nor easy as it needs to be. While standards for sharing of basic data exist, there is a difficult challenge with affirming the identity of the same patient across two institutions.
It is certainly feasible on a case by case basis to do this, but across the thousands of patients needed to do a good retrospective analysis, the problem becomes a lot more difficult. Again, patient privacy issues must be respected when conducting cross institutional analyses.
AR: Q4. In contrast to pharmaceutical industry, the providers and payers do have a good amount of healthcare data. What are your thoughts on the state of Analytics in those industries?

AR: Q5. During drug development and clinical trials what are the major ways in which Analytics can assist decision making?

With better information and analytics available today, the “discovery” at the time of an interim analysis of a low outcome rate in the control population should not occur. The midstream changes to study numbers or enrollment criteria required by these findings are expensive, and impact the eventual analyses, and are much more avoidable with better data and preliminary analytics.
Second part of the interview
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