Pharmaceutical Commerce - April 2010 - (Page 21)
The Patient-Level Approach to Studying Healthcare
Longitudinal patient-level data answer pressing questions about patient safety, outcomes and medication acceptance
By Jody Fisher, SDI Health
1. Non-patient-centered, aggregated prescription data from pharmacies 2. Small-sample primary market research.
The country’s dialogue about the healthcare system mostly centers on the patient. Questions about the usefulness of electronic medical records, the need for transparency of information, and the ability to link information across healthcare entities have, in many ways, already been solved by the biopharma industry. Technology and capabilities in these areas have been growing exponentially for over a decade in the form of longitudinal patient-level data studies. The biopharma industry itself is one of the main beneficiaries of (and supporters of) longitudinal studies. These studies can
reveal vital information on how well a drug has been accepted in the healthcare community and by patients, the interplay between drug choices and diagnostic testing, or the patterns of first-line or second-line therapies (prescribing preferences) by providers. But increasingly, longitudinal studies are benefiting the community of healthcare providers, the insurers, and healthcare policy analysts. Prior to the availability of longitudinal patient-level data, manufacturers and other healthcare concerns were effectively tied to two tools to help assess the needs of, and utilization by, patients:
The first tool had the benefit of a large sample but lacked transparency to patient behavior since the information commonly captured details about the prescriber, payer, pharmacy, and product, but sliced off any patient-identifiable information, making it impossible to infer any behaviors about the individual filling the prescription. The second tool had the benefit of being highly customized to the business need and problem, but was limited in terms of available sample, time to conduct the study, and, of course, cost to produce the results. Patient-level data studies have progressed rapidly since their emergence in the late 1990s, having been driven mainly by three factors:
• The accepted standardization of electronic data interchange for use across the payment system • The increased capacity to store complex
records by the billions, as well as the speed to transfer the information quickly across distance • The influence of HIPAA and patient key methods that ensure patient anonymity while still tracking the patient consistently over time.
While the first two factors were necessary and foundational, the creation of patient key systems was the most significant in terms of the measurement improvement that it afforded. The patient key is a unique code assigned to each patient based on information commonly found on healthcare claims. The same key is created every time a patient’s information is entered. The patient key protects privacy while also allowing de-identified patient activity to be tracked over time and between different healthcare settings. The ability to track patients over time and across dimensions was the genesis of patient-level data studies, creating a powerful way for pharmaceutical companies to understand the behaviors of the true end-users of their products without having to rely on small-sample primary research methods. Availability of patient-level data has grown across dimensions (data sources) over the past decade. The first phase, the single-dimension view, was based on prescription claims data. The second phase, the multi-dimensional view, includes data from a variety of healthcare provider settings. We’ll look at the capabilities of each phase in turn.
PHASE I: THE SINGLE-DIMENSION VIEW
Initially, patient-level data were limited to activity in retail pharmacies, or, more specifically, the information provided on prescription claims. De-identified patients could be tracked across time and across pharmacies, but the data were limited to this single channel of their activity. Healthcare organizations started to develop “best-practice” approaches in deploying patient-level data studies. Several study types became the norm, increasing in complexity as study design methods became well known and accepted. Source of business Source-of-business studies classify prescriptions as distinctly new, continuing, or switch/add-on, providing more specific and truer measures of each patient’s prescription activity. Source-of-business studies are used to more accurately monitor prescription sales growth. An increasing volume of prescriptions filled by patients new to therapy, which can only be determined by using patient-level data, is a much better indicator of growth than new prescription (NRx) gains. The traditional NRx metric measures only the numcontinued on page 22 >
APRIL 2010 21
Table of Contents for the Digital Edition of Pharmaceutical Commerce - April 2010
Pharmaceutical Commerce - April 2010
Business & Finance
BrandMarketing | Communications
Supply Chain | Logistics
Packaging & Drug Delivery
Legal | Regulatory
Executive Training & Development
Editorial Index & Meetings
Pharmaceutical Commerce - April 2010