Learn more about Stan Pestotnik, MS, RPh

How to Use Data to Improve Quality and Patient Safety

Healthcare organizations have worked hard to improve patient safety over the past several decades, however harm is still occurring at an unacceptable rate. Though the healthcare industry has made efforts (largely regulatory) to reduce patient harm, these measures are often not integrated with health system quality improvement efforts and may not result in fewer adverse events. This is largely because they fail to integrate regulatory data with improvement initiatives and, thus, to turn patient harm information into actionable insight.
Fully integrated clinical, cost, and operational data coupled with predictive analytics and machine learning are crucial to patient safety improvement. Tools that leverage this methodology will identify risk and suggest interventions across the continuum of care.

Healthcare Safety Culture: A Seven-Step Success Framework

Preventable patient harm costs healthcare billions annually, making strategies to improve patient safety an imperative for health systems. To improve patient safety, organizations must establish a safety culture that prioritizes safety throughout the system, supports blame-free reporting of safety events, and ensures that healthcare IT solutions functions and accessibility align with safety goals.
A sociotechnical framework gives health systems a seven-part roadmap to improving patient safety culture:

Leverages qualitative and quantitative data.
Doesn’t rely on HIMSS stage levels to tell the complete safety picture.
Gives frontline clinicians a voice in decision making.
Makes IT solutions accessible to non-technical users.
Encourages frontline clinicians to report safety and quality issues.
Treats a safety issue in one area as a potential systemwide risk.
Performs thorough due diligence before taking safety IT solutions live.

Preventing Medication Errors: A $21 Billion Opportunity

With a potential industry-wide savings of almost $21 billion and an impact on more than seven million patient lives, preventing harmful medication error is a significant improvement opportunity for health systems. Also known as adverse drugs events (ADEs), harmful medication errors comprise about 37 percent of all medical harm. Approximately 50 percent of ADEs are preventable, making their reduction a highly impactable area of patient safety.
Current data and analytics workflow tools are making ADE surveillance, monitoring, and prevention increasingly more effective with four key capabilities:

Perspective surveillance for ADEs and identification of previously undescribed ADEs.
Identification of the root cause of many ADEs by drug class.
Prescription at appropriate doses for patients with compromised kidney or liver functions.
Identification of different types of harm to find causes.

Introducing the Health Catalyst Monitor™ Patient Safety Suite: Surveillance Module

Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. The Health Catalyst PSO creates a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.

Improving Patient Safety: Machine Learning Targets an Urgent Concern

With over 400,000 patient-harm related deaths annually and costs of more the $1 billion, health systems urgently need ways to improve patient safety. One promising safety solution is patient harm risk assessment tools that leverage machine learning.
An effective patient safety surveillance tool has five core capabilities:

Identifies risk: provides concurrent daily surveillance for all-cause harm events in a health system population.
Stratifies patients at risk: places at-risk patients into risk categories (e.g., high, medium, and low risk).
Shows modifiable risk factors: by understanding patient risk factors that can be modified, clinicians know where to intervene to prevent harm.
Shows impactability: helps clinicians identify high-risk patients and prioritize treatment by patients who are most likely to benefit from preventive care.
Makes risk prediction accessible: integrates risk prediction into workflow tools for immediate access.