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Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes

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The Adverse Childhood Experience (ACE) study conducted by the CDC and Kaiser Permanent showed a strong correlation between ACEs and negative health outcomes later in life (e.g., risky health behaviors, chronic health conditions, and early death). ACE scores help paint a more complete picture of a person’s health history—a more comprehensive data snapshot of the entire patient.

Given that ACE scores build better data sets and machine learning relies on high-quality data, health systems should incorporate these nutrient-rich data sources into their machine learning models to better predict negative health outcomes, allow for earlier interventions, and improve outcomes.

Healthcare machine learning is evolving to use ACE scores and lifestyle data (e.g., eating habits) to improve population health management.

Publicado en: Atención sanitaria
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Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes

  1. 1. Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes Michael Mastanduno, PhD Yannick Van Huele, PhD
  2. 2. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning in Healthcare Machine learning in healthcare uses data, an algorithm, and a model to predict an event and suggest interventions that can improve the outcome of that event. A machine learning model for a health system could be designed to predict, for example, who in the hospital is likely to get a central line-associated bloodstream infection (CLABSI). Clinicians could then pay special attention to infection control best practices for those identified patients.
  3. 3. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning in Healthcare Initially, the machine is fed historical data (e.g., the health system’s patient attributes for those that did and did not get a CLABSI over the past two years), which an algorithm uses to learn relationships (e.g., historical CLABSI rates relative to patient age and comorbidities, duration of catheter insertion, and catheter type used).
  4. 4. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning in Healthcare This is the essence of the machine learning workflow, which stores the relationships and applies them to make the prediction (e.g., given this patient’s similarities to all the historical CLABSI cases, he has a 75 percent chance of getting an infection today). The model is trained with new data as it becomes available (e.g., CLABSI cases over the next six months), which improves the reliability of future predictions.
  5. 5. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What is Machine Learning? The components and processes of a machine learning model to predict a healthcare outcome.
  6. 6. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning in Healthcare Health systems can use machine learning to predict sepsis, the likelihood of readmission or missing an appointment, and dozens of other clinical and operational conditions. From the workflow described on the previous slide, it’s evident that “nutrient-rich” data sources are necessary to feed predictive algorithms in a machine learning model that’s designed to improve health outcomes.
  7. 7. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Machine Learning in Healthcare An important health system objective is making accurate predictions, which relies on capturing a data snapshot of the entire patient. Adverse Childhood Experience (ACE) scores fill one of the significant gaps health systems typically don’t have data for.
  8. 8. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Data Headwaters of ACE Scores From 1995 to 1997, the CDC and Kaiser Permanente conducted a landmark survey of more than 17,000 people to learn about their early childhood experiences (e.g., abuse, relationships, drug and alcohol use, etc.) and current health statuses. Participants were asked about ten types of childhood trauma related to abuse, household challenges, and neglect, and were assigned an ACE score on a scale from 0 to 10.
  9. 9. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Data Headwaters of ACE Scores The participants were also asked about personal and family health. The study showed a strong correlation between high ACEs and negative health outcomes later in life, including risky health behaviors, chronic health conditions, reduced lifetime income, and early death. While national efforts aim to prevent child abuse altogether, much can be done later in life to prevent further consequences from those early experiences.
  10. 10. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Data Headwaters of ACE Scores The opportunities for collecting ACE data are relatively rare, either during childhood or a primary care visit later in life if the doctor is involved with a data-collecting program. But ACE scores only need to be collected once; they never change, and, as the CDC puts it, provide tremendous insight into a “person’s future violence victimization and perpetration, and lifelong health and opportunity.”
  11. 11. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using ACE Data to Improve Individual and Community Health ACE data is typically used in public health programs for state- and community-wide prevention efforts. In the context of machine learning, health systems should use it to benefit individual patients, so they can flag them as high risk, treat them appropriately, and hopefully prevent ACE-related conditions from surfacing. Family Health History and Health Appraisal questionnaires are readily available instruments for establishing ACE scores.
  12. 12. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using ACE Data to Improve Individual and Community Health These are new sources of information to consider and systems should broadly adopt surveys as instruments for improving population and individual health. The better picture organizations can paint of a person’s health history, the better they can predict the need for future interventions. The better the incoming data, the better the predictions.
  13. 13. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using ACE Data to Improve Individual and Community Health Here’s an example of how health systems can use ACE scores and machine learning to improve patient outcomes: A patient named Alex is admitted to the ED at a hospital where a machine learning model is used to predict opioid addiction risk. The model discovers the strong relation between high ACE scores and opioid abuse in the historical data, and flags Alex as being high risk for addiction. Clinicians act on this information by avoiding an opioid prescription but also treat the underlying factors that make Alex more prone to abusing opioids.
  14. 14. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Health Systems Need Feature Engineering on All Source Data It’s possible to have bad data or too much data, so feature engineering separates the wheat from the chaff. Feature engineering encodes data into formats that are useful for machine learning. For example, if a health system thinks a patient’s addiction risk may have a seasonal component, then it must convert the date column from “August 14, 2014” to “August.” Once the system gets the data, it can select how it will feed it into the model, and whether to toss, keep, separate, or combine certain variables. This applies to all source data, including ACE scores.
  15. 15. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improve Outcomes with ACE Data As health systems rely more heavily on machine learning, they must keep in mind that a machine learning model is only as good as the data it receives. Machine learning in healthcare depends on high- quality data to improve outcomes; ACE scores help build better data sets. Incorporating “nutrient-rich” data sources, such as information about ACEs into machine learning models, can improve their ability to predict negative health outcomes, therefore allowing for earlier interventions.
  16. 16. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Improve Outcomes with ACE Data Clinical data collection is moving in the right direction. Today, organizations have data that can be used for machine learning on current problems; in the future, ACE scores, eating habits, and lifestyle data will all be combined to predict diseases earlier and significantly improve population health management.
  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. Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes How Machine Learning in Healthcare Saves Lives Levi Thatcher, Director of Data Science How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient Outcomes – Mike Dow, Technical Director; Levi Thatcher, Director of Data Science How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare Levi Thatcher, Director of Data Science There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future of Predictive Analytics in Healthcare – Dale Sanders, Senior VP, Strategy Three Approaches to Predictive Analytics in Healthcare David Crockett, Ph.D, Senior Director of Research and Predictive Analytics
  19. 19. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Mike Mastanduno joined Health Catalyst in November of 2016 as a Data Scientist. He received his PhD from Dartmouth College in Biomedical Engineering, where he designed hardware and software tools to aid in the early diagnosis of breast cancer. Mike’s dissertation culminated in a 60-patient clinical trial to evaluate the technology he had developed. Mike then went on to a postdoctoral fellowship at the Stanford School of Medicine where he won a National Institute of Health award to study medical imaging of ovarian cancers. Since joining Health Catalyst, Mike has been focused on outcomes improvements through machine learning. Mike has had a hand in development of a high-performance heart failure readmissions risk model, a service line predictor that saves greater than 1 million dollars per year, and a sophisticated statistical model to find high-cost imaging utilization. Mike’s current focus is on over-utilization and image processing. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Michael Mastanduno, PhD
  20. 20. © 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Yannick Van Huele joined Health Catalyst in May 2017 as a Data Science Intern. Prior to coming to Health Catalyst, he was a graduate student at the University of Washington where he studied algebraic number theory and received a PhD in Mathematics. Yannick Van Huele, PhD

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