Navigating the complexities of healthcare data types
Healthcare and life sciences industries are leveraging the current abundance of data to inform business decisions at critical milestones. However, navigating the landscape of healthcare data has become increasingly challenging as the volume of data expands across multiple data types, leaving organisations to wonder which datasets are the appropriate sources of the solutions they seek. Moreover, the business questions life sciences professionals face are varied and complex. They need data insights for use cases ranging from asset evaluation to sizing markets across multiple indications, merger and acquisition opportunities, and generating the evidence stakeholders require for approval.
There is no one data type or tool that will address all your needs all the time, but selecting the right foundational dataset is key. Thinking critically about what types of data will address your business questions, what gaps exist and what additional data may be required will improve clarity, drive efficiency and support successful data strategies for faster, well-informed decisions.
Open and closed claims: Two foundational data types
Closed claims and open claims data represent two foundational and distinct approaches to collecting healthcare information, each offering unique benefits and applications.
Advantages of open claims
Open datasets are collected from various networks supporting points of care within the healthcare ecosystem. This type of data typically offers the broadest possible sample, and can capture patient journeys over a long period of time. These sources often allow a granular view of payer influence and the financials associated with the delivery of services.
The advantages of open datasets lie in their broader patient population coverage, transparency regarding payer identity and influence on the financial aspects of care delivery, and minimal lag between healthcare events and their appearance in the data.
Due to the way in which the data is collected, open claims can contain gaps and will not capture patient events occurring outside of the data sample network. For example, a patient may visit a primary care physician, a hospital emergency room and a cardiologist related to a health event. In an open dataset, it is possible that one of the three points of care are part of the data sample such that we see the patient’s primary care physician visit and cardiologist visit, but not the ER visit. Proven techniques exist to close data gaps and construct stable patient cohorts to mitigate the risk of missing data, but it adds an additional layer of complexity to the analysis.
Open datasets are more advantageous for:
- Analysing healthcare trends across diverse populations
- Identifying broad utilisation trends
- Examining sub-national trends at geographically granular levels
- Revealing the effects of managed care utilisation management tactics
- Informing sales and marketing operations
Comprehensive data with closed claims
Closed datasets are sourced from data provided by health plans, yielding a comprehensive view of patient activity during the patient’s enrolment period. Closed datasets ensure nearly complete visibility into patient care events submitted to their health plan, facilitating highly accurate insights into healthcare utilisation and outcomes. Due to the way in which the data is collected, the closed dataset sample size is smaller, but provides a more detailed and patient-centric view that is ideal for precise analysis.
A key criterion for evaluating the quality of a closed dataset is its representativeness in relation to the overall population of interest. Analysis performed on a highly representative closed dataset more convincingly projects to the population. Analysis performed on a closed dataset lacking sufficient representation geographically or socio-economically must explore the implications for unrepresented populations.
Closed datasets are more advantageous for:
- Health Economics Outcomes Research (HEOR)
- Comparative effectiveness
- Clinical trial design
- Clinical operations
- Patient journeys/natural histories
Choosing a claims dataset
For professionals in the pharmaceutical industry, the choice between open and closed datasets depends on the nature of the research questions and business objectives. Commercial teams prioritise breadth, longevity and recency of data, making open datasets more suitable for market analysis and launch strategies. Clinical teams focusing on patient journeys/natural histories, healthcare utilisation and health economics outcomes research often benefit from the depth and representativeness of the patient population in closed datasets.
The combination of both open and closed datasets can offer the best of both worlds, providing comprehensive insights into patient behaviours and healthcare trends. This hybrid approach enables clients to leverage diverse data sources for more nuanced analyses, such as the need for precision around outcomes or utilisation along with the payer impact.
Building on your foundation
Once industry professionals choose the appropriate data foundation, they must consider the possibility that additional data sources must be integrated. Do the key data elements in either open or closed claims include all the information necessary to address the research questions and business objectives? Expanding on the foundational dataset to get a more holistic perspective can drive deeper insights to guide business decisions. Two potential data additions to your foundation below:
Social Determinants of Health (SDoH), including lifestyle, living conditions, job and income access have a material impact on patients’ ability to access and remain on treatment. Understanding how these factors affect your patient population is imperative to making sound business decisions.
Lab Data can provide additional insights into treating physician behaviours, patient diagnosis, follow-up visits, diagnostic test orders and results across a broad range of disease states.
Unlocking closed claims potential with PatientSourceComplete
In this landscape of data-driven decision-making, Symphony Health presents PatientSourceComplete, a closed claims dataset with a comprehensive suite of features and benefits designed to empower informed decisions and drive positive outcomes. One of the primary advantages of PatientSourceComplete is its payer diversity, encompassing over 160 health plans, including commercial as well as managed Medicaid and Medicare Advantage. This breadth ensures a rich and diverse pool of data, offering insights into various healthcare landscapes and disease progression across the United States.
PatientSourceComplete offers the flexibility of external linking, and is the only closed dataset that comes with the Synoma® token and access to its vast partner network. This allows for the creation of custom de-identified datasets by seamlessly connecting source data with other relevant datasets, as described above. This interoperability enhances the depth and breadth of analysis, facilitating nuanced insights and enabling more targeted decision-making processes.
Our closed claims dataset supports quality enhancement initiatives within Symphony Health. Through thorough analysis of health outcomes and resource utilisation patterns, stakeholders can identify areas for improvement, refine care delivery protocols and enhance the overall quality of care provided to patients.
Innovating for improved outcomes
PatientSourceComplete represents a groundbreaking tool for healthcare professionals and organisations seeking to unlock the potential of data-driven decision-making. With its diverse dataset, comprehensive coverage of healthcare activities, and array of analytical capabilities, PatientSourceComplete empowers stakeholders to drive innovation, improve patient outcomes, and effectively mitigate risks in today's complex healthcare landscape.
To find out more about PatientSourceComplete and how Symphony Health can address your data needs, connect with us today.