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MSA HCDM white papers
What's happening in healthcare data management...

THE MSA APPROACH TO LEVERAGING ANONYMOUS PATIENT LEVEL DATA

Measure Treatment Efficacy/Discover New Market Segments

Analyzing aggregate anonymous patient data over time enables pharmaceutical companies (pharmacos) to better understand treatment efficacy and discover new market segments for generating revenue.

Patient data-matching, a fundamental and critical success factor for the longitudinal alignment of anonymized health information, can, however, be a daunting challenge given the complexity, sensitivity, volume, and heterogeneity of patient healthcare data, as well as the use of primitive technologies.

Learn how MSA’s HIPAA-compliant/HITRUST-certified, state-of-the-art technologies help pharmacos use patient data to drive strategies, ensuring the delivery of the right therapy to the right people.

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UNCOVER THE FULL PATIENT JOURNEY

Addressing Privacy and Data Integration Challenges: Proven DE-IDentification and Data Management Solutions from MSA

Large volumes of healthcare data – with varying formats and quality – present unprecedented privacy and data management challenges. Proper data management approaches protect patient privacy while organizing, integrating and managing access to quality data for all relevant stakeholders, and speed the time from data collection to actionable insights.

MSA’s full-service, flexible solutions range from de-identifying patients to a full patient journey data integration and data management solution. MSA leverages “the right people” – data management experts, “the right software” – patented de-identification and data management/integration technology, and “the right underlying environment” – HIPAA/HITRUST-Certified environment to provide high-quality, anonymous patient-level  longitudinal datasets (APLD) for analytics.

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Aggregating Data

CHANGING THE PARADIGM OF SPECIALTY PHARMACY DATA ANALYTICS

Aggregating Special Pharmacy and Other Data

Specialty Pharmacies with the ability to aggregate data in ways that drive informed, real-time decisions have a competitive advantage. Aggregating internal data with data from other sources gives Specialty Pharmacies another means to measure both the effectiveness of pharmacy care and product performance, resulting in improved pipelines, products, and strategies. By mining patient-level outcomes data, a Specialty Pharmacy can analyze the efficacy and safety profile of a specific treatment.

Effectively managing clinical care demands better integration of pharmacy data and other data, particularly Lab Results data. Understanding the use and value of data from multiple sources facilitates the improvement of Specialty Pharmacy patient outcomes, benefiting all stakeholders.

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IMPACT OF DATA QUALITY ON A SPECIALTY PHARMACY PRODUCT LAUNCH

More accurate analyses, better informed decisions

Product launches have become more complex as the healthcare environment becomes more dynamic and diverse. Creating a holistic patient view requires bringing together diverse patient data typically spread across multiple datasets, into one coherent journey. This integration can be very complex and difficult, especially if there are gaps and inconsistencies in the patient data.

Data quality is both tangible and measurable, and has a direct impact on revenue. Providing the right information at the right time pays off in terms of operational costs, efficiency, and of course, sales. Pharma manufacturers using quality data know how and where their products are being prescribed, and how patients view and use their products. Properly managed, this data can provide treatment adoption and adherence information, supporting a successful launch.

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Quality Process

HOW TO IMPROVE DATA QUALITY

A Key Differentiator: MSA’s Quality Process puts a transparent focus on technical excellence

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.

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SEEING THE BIG PICTURE IN SPECIALTY PHARMACY DATA

Meaningful data connections give a competitive edge

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.

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DE-IDENTIFICATION: WHEN SAFE HARBOR IS TOO RESTRICTIVE

Effectively anonymize data without diminishing analytic utility

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?

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