Patient Safety

Using Deep Risk Management to Improve Patient Safety

Based on a ten-year review of claims, one of the largest medical professional liability (MPL) insurance companies in the US declared in a 2020 report that, “general claims trends suggest only modest gains in patient safety over the 10-year period.”  This sobering statement can be better understood by examining the efforts themselves.

Notably, the patient safety review processes – including root cause analyses – are retrospective.  It is critical to look back on medical errors and build tools to prevent them going forward.  However, there have been two fundamental problems with these traditional processes:

1. 1. Key data collected has too often been poorly categorized, or worse, not collected at all.
   2. Lack of incentives to implement suggested preventative risk management techniques. 

The MPL industry set out to improve patient safety many years ago.  It invested in personnel, companies, technology, consultants, and data. It built discounts into its policies to encourage compliance with recommendations.  But the data and the results were inconclusive, and the savings insignificant.  Outside of MPL (and a host of regulatory requirements indirectly related to patient safety) – risk management has been virtually nonexistent.

Value-based care has opened access up to voluminous data sets – from hospitalizations to lengths of stays to quality scores.   Advanced approaches to improving patient safety can leverage such data.  The more “real time” the data feeds, the more precise predictive analytic models become, and the better AI can learn.

The opportunity now exists to collect significantly better data, and the incentives now exist to implement meaningful change.  A deeper dive into any value-based care model reveals significant risk management potential.

The overlap between financial risk and professional liability risk is considerable.  Well-run models reduce complications, hospitalizations, medical expenses and pain and suffering.  The beneficiaries are the patients, the model participants and the MPL industry.

Perhaps the MPL industry will pivot and engage in the next phase of patient safety.  Either way, at-risk contracts demand partnerships and data that will foster a new age of risk management.  Deep risk management.

The coalescence of claims and EHR data; benchmarking; patient engagement experience survey results; patient portals; and even “burnout” indicators – presents an unprecedented opportunity to propel risk management to a new level.

DRM coordinates, and helps clients navigate, several highly complex fields – delivering cutting-edge products and services.  DRM can equip medical practices of all sizes with the tools to compete with even the largest systems – all while helping them understand expensive cost drivers and improving care coordination.