Pharmaceutical companies use anonymous longitudinal patient data (APLD) to track patient treatments over time and to answer specific business questions. Inconsistencies and/or gaps in data are a major challenge to analytics. MSA has developed advanced tools that identify and fix gaps and formatting inconsistencies in APLD, making the data more robust.
People can only make the right data-driven decisions if the data they use is correct. Good data quality is a strategic asset that provides a competitive advantage. Having appropriate data quality processes in place directly correlates with an organization’s ability to make the right decisions. Successful data quality processes require a holistic, end-to-end approach and a company culture that recognizes the importance of data quality for generating insights.
Getting the most value from Specialty Pharmacy data is critical to monitoring providers, tracking patients and services, and measuring performance. The data can be used to develop commercialization plans, select patient and physician populations for specific therapies, and measure the effectiveness of marketing and sales resources. However, the rapid growth of the specialty pharmacy market has resulted in a large volume of complex data that can be overwhelming.
Pharmas face the challenges of identifying the data most important to the analysis based on availability and then, once selected, pulling it all together to gain an understanding of the obstacles and opportunities to improving healthcare.
Healthcare and Life Science organizations are striving to demonstrate outcomes that reduce cost, improve the health of populations, and prove their value to stakeholders. At the same time, privacy concerns are increasing as healthcare data grows faster than ever from an increasing array of sources, including claims, clinical, hospital, and research. How can your organization effectively anonymize data to meet your privacy obligations without diminishing the analytic utility of the data?
What is the difference between encryption, data masking and de-identification and when is it considered best practice to use each? Which method of de-identifying data – Safe Harbor or Expert Determination – is better suited to derive value and new insights from healthcare data for secondary purposes?