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Improving Patient Safety: Machine Learning Targets an Urgent Concern

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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:

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

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Improving Patient Safety: Machine Learning Targets an Urgent Concern

  1. 1. Improving Patient Safety: Machine Learning Targets an Urgent Concern
  2. 2. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety Patient harm is a persistent and urgent healthcare concern that directly impacts the patient experience and overall outcomes. According to recent estimates, one in three hospitalized patients experiences preventable harm, and over 400,000 individuals per year die from these injuries. As the third-leading cause of death, preventable harm costs health systems more than $1 billion annually.
  3. 3. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety As health systems seek ways to improve patient safety, many organizations are looking to patient harm risk assessment tools that leverage machine learning. This article describes how machine learning powers better risk prediction tools, with the eventual goal of helping clinicians identify safety concerns before the patient harm occurs.
  4. 4. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Better Risk Prediction Tools Consider Whole-Patient Risk Previous risk prediction models were limited because they were developed using populations different from the populations for which the tools were being used. Another downside to these out-of-the-box models (such LACE, which predicts readmissions) is that they were often trained on data that was 15 to 20 years old. These generalized tools were also siloed in stand-alone prediction systems.
  5. 5. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Better Risk Prediction Tools Consider Whole-Patient Risk Patients tend to be at risk for a variety of negative outcomes, so with a siloed risk assessment approach, clinicians miss opportunities to manage or prevent harm. The patient safety community has devised a whole patient measure of safety to address siloing in measuring patient harm. The same concept needs to be applied to risk prevention.
  6. 6. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Better Risk Prediction Tools Consider Whole-Patient Risk A better, machine learning-powered patient safety tool uses health system data to assess whole-patient risk, giving clinicians a compre- hensive view of patients who are at risk for a safety event, including identifying the particular event(s), as well as modifiable risk factors.
  7. 7. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning Enables Timely Risk Identification This new generation of machine learning-based patient safety tools will close the loop between information and action, as the software not only forecasts the likelihood of harm, but also the most important clinical actions to lower that patient’s risk, helping the clinician make an informed intervention decision. Clinicians will be able to predict harm before it occurs, know who in their patient population is at risk, understand which of the patient’s modifiable risk factors need to change, and be able to make timely interventions.
  8. 8. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities To successfully reduce rates of patient harm, today’s patient safety risk assessment tools must have five core capabilities: 1. Identifies risk 2. Stratifies patients at risk 3. Shows modifiable risk factors 4. Shows impactability 5. Makes risk prediction accessible
  9. 9. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities 1: Identifies Risk • Provides concurrent daily surveillance for all-cause harm events using literature-based triggers to show how many patients in a health system population are at risk for safety events (e.g., CLABSI).
  10. 10. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities 2: Stratifies patients at risk • Places at-risk patients into risk categories (e.g., high, medium, and low risk).
  11. 11. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities 3: Shows modifiable risk factors • By understanding a patient’s modifiable risk factors and the degree to which they can be impacted, clinicians know where to intervene to prevent harm. • Modifiable risk factors for a condition such as CLABSI include line days, number of lines, bathing rates, and compliance with bundles (interventions that, when implemented as a group, have a greater effect than individual interventions alone).
  12. 12. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities 4: Shows impactability (Figure 1) • Offers clinical decision support that helps clinicians identify high-risk patients and prioritize treatment by patients who are most impactable (most likely to benefit from preventive care). Figure 1: An effective patient safety tool shows how likely a patient is to be impacted by an intervention
  13. 13. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities 5: Makes risk prediction accessible • Integrates risk prediction into workflow tools for immediate access.
  14. 14. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improving Patient Safety with Five Must-Have Machine Learning Capabilities Machine learning capabilities help organizations get upstream of the risk before a patient is harmed or at risk of harm. Eventually, predictive patient safety tools will advance the way organizations mitigate patient harm by recommending interventions for modifiable risk factors and documenting those interventions. The next goal is to integrate with cost management tools to attribute cost and recommend data-driven, cost effective interventions.
  15. 15. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning for Patient Safety: A Much-Needed Solution With the current, unacceptably high rates of preventable patient harm occurring in health systems, improving patient safety is a critical healthcare mission. Machine learning will drive the solution by enabling safety surveillance tools that use health system data to identify patients at risk, identify patients’ modifiable risk factors and impactability, and, eventually, recommend the most cost- effective interventions. Healthcare’s best chance of improving patient safety and outcomes lies in predicting harm and taking action to prevent it.
  16. 16. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning for Patient Safety: A Much-Needed Solution As Don Berwick, MD, MPP, president emeritus and senior fellow at the IHI, explained in his keynote address at the 2017 National Patient Safety Foundation Patient Safety Congress, healthcare has a lot of work to do to improve patient safety. “There’s an illusion that we’ve worked on safety,” Berwick said, and added that healthcare hasn’t developed real insight into patient harm and ways to prevent it. With a comprehensive, concurrent data- driven approach to patient harm, machine learning promises to transform patient safety from illusion to reality.
  17. 17. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information: “This book is a fantastic piece of work” – Robert Lindeman MD, FAAP, Chief Physician Quality Officer
  18. 18. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Link to original article for a more in-depth discussion. Improving Patient Safety: Machine Learning Targets an Urgent Concern How to Use Data to Improve Patient Safety Stan Pestotnik, MS, RPh,VP, Patient Safety Products; Valere Lemon, MBA, RN, Sr. Subject Matter Expert Patient Flight Path Analytics: From Airline Operations to Healthcare Outcomes Dale Sanders, President of Technology; David Crockett; Justin Gressel The Top Seven Analytics-Driven Approaches for Reducing Diagnostic Error and Improving Patient Safety – Kirstin Scott; Tracy Vayo Prescriptive Analytics Beats Simple Prediction for Improving Healthcare David Crockett Lowering Sepsis Mortality and Length of Stay: One Hospital System’s Story Health Catalyst Success Story
  19. 19. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Prior to Health Catalyst Stan has held several executive, clinical and research roles. Most recently he was the Chief Strategy Officer for Pascal Metrics a federally-certified Patient Safety Organization. Prior to that Stan was the founding CEO of TheraDoc, which he led for 10+years until its acquisition. For 2+ decades Stan was a clinician, researcher and educator at the University of Utah School of Medicine, College of Pharmacy and at IHC- LDS Hospital. Stan is clinically trained as a pharmacist specializing in infectious diseases as well and has an advanced degree in medical informatics specializing in clinical surveillance and expert system decision support technologies. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Stan Pestotnik, MS, RPh

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