From the October 2016 issue of HealthCare Business News magazine
Knowing the right question to ask is perhaps the most difficult aspect of data analytics. Practitioners can use their domain knowledge to frame a problem or a question that needs to be answered. Data scientists can find correlations in data, but they might be clueless at understanding causation. Working together, data scientists can help practitioners see patterns they might otherwise miss, and practitioners can help data scientists understand the meaning and context behind the data.
Data can be old
Just 5 percent of patients account for almost half of all health care costs. If only we could find those high-risk patients and focus all of our efforts there. The problem is, using last year’s claims data can be ineffective at predicting this year’s high-cost patients. It won’t be the same patients, but instead a different 5 percent of individuals from the year before. Older data can provide context and supplement predictive models, but good predictions need real-time data that stays up to date as a care episode evolves.
The quality and arrival time of data is dependent on both the work patterns of practitioners on the ground and the effective integration and deployment of telehealth technology. Here, practitioners and data scientists can work together to examine workflows, patient interactions and care episode dynamics, to implement systems that are kept current, so that delays in data arrival don’t get in the way of patient care.
Data can have gaps
With the move to value-based reimbursement, practitioners must coordinate care across care settings. Data scientists should be deriving insights from the entire care episode, but too often data access is limited to just the care settings within the practitioner’s own organization. These gaps ultimately undermine the accuracy of analytical models developed by data scientists.
The technology required for secure exchange of clinical data exists, but access is often blocked by the absence of a workable legal framework for data sharing. These challenges can only be addressed by practitioners, who must answer questions about what data should be shared, how it will be used, and how to balance care coordination and patient privacy needs with the competitive interest of their own organizations.
Through hard work and collaboration among practitioner organizations, effective data use agreements can be established to enable data scientists to leverage information now locked away in system silos.