Data is pivotal in shaping patient care and driving industry innovation in today's healthcare landscape. Insights from healthcare data can provide a comprehensive view of a patient's journey, optimise resource allocation, and improve strategic planning for healthcare systems and providers. However, transforming raw data into actionable insights requires technical expertise and an understanding of the inherent challenges. Without this, data analysis may lead to inaccurate conclusions, contributing significantly to the estimated 470 billion Euros lost annually in the healthcare industry to fraud, waste, and abuse (FWA).
The Value and Limitations of Claims Data
Claims data, which is primarily designed for reimbursement purposes, can offer a snapshot of healthcare activities. However, using this data to generate insights can introduce biases and other issues. One such issue is 'missingness,' ranging from minor omissions to significant data gaps. This missing data can undermine the validity of any insights derived. For instance, a national health system's analysis of market strength for hip replacements was skewed due to missing data from direct billers, leading to an underestimation of several key providers.
Addressing Data Quality and Missingness
Healthcare and pharmaceutical companies frequently overlook the extent of missingness and its impact on data quality. Issues such as incorrect data hashing further complicate matters, requiring a deep understanding of the root causes and proactive measures to address them. While some organizations resort to purchasing additional data to mitigate these challenges, this approach does not fully resolve the problem. Advanced analytics techniques, including machine learning (ML), can suffer from these data quality issues. For instance, the marginal value of new data records diminishes over time, especially if biases are not corrected, resulting in inaccurate ML model outputs. However, selectively utilising high-confidence data and discarding unreliable data can enhance the accuracy and reliability of these models.
To fully harness the potential of healthcare data, it is crucial to adopt a comprehensive approach to data analysis. This approach should include robust data sourcing, advanced analytical techniques, and a keen awareness of privacy and contractual limitations. By doing so, we can generate more reliable and actionable insights, whether we're addressing basic questions or employing advanced predictive analytics.
Source Credit: HealthcareITtoday
Image Credit: iStock
