Real-World Data (RWD) is playing a bigger role than ever with regulatory bodies looking for Real-World Evidence (RWE) to support pre- and post-market decisions. To date, the pharmaceutical industry has primarily focused on connecting only the most easily accessible data sources to generate RWE: EHR and claims data. Now, however, MSA Healthcare Data Management’s ability to attain and link RWD from across the entire patient journey is fueling the research and analyses necessary to draw RWE conclusions that answer real-world questions and predict outcomes.
Differences in lived experiences, opportunity, and exposure to environmental stressors and toxins among racial/ethnic groups can be missed when clinical trials fail to test interventions on diverse participants. This lack of/or low representation of racial/ethnic minority groups in clinical trials poses a grave problem, potentially resulting in the development of interventions that do not translate well into real-world use and are not efficacious in different populations.
Through the use of real-world evidence, MSA HCDM helps pharma pinpoint high incidence rates and develop strategies for targeting/educating the most appropriate physicians and patients. By enriching and integrating disease prevalence data with other data sources MSA HCDM solutions optimize clinical trials via more efficient site and patient identification.
Pharma companies are facing the biggest data challenges in history. The growth of Independent Delivery Networks (IDN) focused on improving quality and outcomes while reducing cost, the growing number of biologics and small molecules that require Specialty Pharmacy (SP) delivery, as well as the increasing use of HUBs to manage product launches as well as savings card and voucher programs results in disconnected data.
A fragmented delivery model increases the likelihood of incorrectly matching a patient across various data sources, making it difficult to visualize the patient journey. Learn how MSA data solutions provide the tools pharma needs to visualize the patient journey – linking clinical lab results, prescriptions, EMR, medical claims, and other data while protecting privacy.
The best partner for the de-identification, integration, and management of patient data provides state-of-the-art technologies and processes, as well as additional benefits, such as unequalled customer service and flexibility.
Why MSA? HiTRUST-certified, MSA meets industry-defined mandates and maintains the highest standards of cyber risk management and patient data loss prevention. MSA’s patented, HIPAA-compliant technologies ensure the highest patient matching rates, on-going high data quality, and more. MSA solves problems, focusing on one customer and one project at a time.
Learn how partnering with MSA for the de-identification, integration, and management of patient data gives organizations the competitive edge.
Analyzing aggregate anonymous patient data over time enables pharmaceutical companies 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 pharma use patient data to drive strategies, ensuring the delivery of the right therapy to the right people.
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.
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.
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.
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?