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Health Analytics


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Course support for Analytics in Action

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Health Analytics

  1. 1. Health Analytics February 2018 Analytics in Action
  2. 2. • Why do healthcare establishments need to develop in-house data science expertise, or become increasingly reliant on external consultants and software editors. • How do the authors explain the lack of specialists in this field? • How do they support their contention that the industry's senior leadership would rather rely on their own instincts rather the data? • What is the nature of the build vs buy dilema? Using It or Losing It? Introduction Martin Heusch and Timothy Mauser, «Using It or Losing It? The Case for Data Scientists Inside Health Care"
  3. 3. Introduction ©2016 L. SCHLENKER Agenda Introduction Definitions Infrastructure Use Scenarios Limits
  4. 4. Introduction • Place - changes in geography, time, physical resources and budget • Platform – enriching how information is produced and consumed • People – modifying the frame of reference • Practice - impacting the reality of management Schlenker (2015)
  5. 5. • The use of data, analysis, and predictive modeling to improve teaching and learning • Analytics models aggregate data in new ways • Help students and institutions understand past, present and future academic performance • Impact on personalized learning, pedagogical practices, curriculum development, institutional planning, and research Health Analytics Technology
  6. 6. • Health care analytics arims to improve clinical care while limiting excessive spending • Healthcare Activities that can be undertaken as a result of data collected from four areas within healthcare;  Claims and cost data,  Research and development (R&D) data,  Clinical data (collected from electronic medical records (EHRs)),  Patient behavior and sentiment data Context Technology
  7. 7. • The journey from fee-for-service to value- based contracts • Understanding the wants and needs of health care consumers • Defining the role of patient experience in marketing • Embracing transparency • Creating patient loyalty in health care organizations Market Challenges Technology NRC Health
  8. 8. • The Assistance Publique-Hôpitaux de Paris has been using data to predict daily and hourly patient admissions • Propeller Health, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends help clients identify novel trade patterns • Flatiron Health has developed a service called the OncologyCloud, based on the idea that 96% of potentially available data on patients with cancer is not yet analyzed • Optum Labs has collected EHRs of over 30 million patients to create a database for predictive analytics to improve healthcare delivery Whose doing it? Technology
  9. 9. • Using collaborative analytics to personalize treatment plans • Analytics can help derive meaningful insights to attract customers, as well as manage costs and risk of health plans • Detect fraud based on analysis of anomalies in patient records • Open up new diagnostic landscapes for the automated interpretation of x-rays, CAT scans, and MRIs Use Scenarios Technology
  10. 10. • 3865 Healthcare Data Scientist jobs • The core data science skillset of machine learning, data visualization, and statistics • Healthcare refers to dozen sub-industries such as hospitals, health plans, pharmaceuticals, medical devices, • The most important information is hidden in unstructured data • The need to work closely with their end users • It is critical to really understand what constitutes success Working in Healthcare Technology D’Avolio, L. (2017) What Data Scientists Need to Learn
  11. 11.  Cutting down administrative costs  Clinical decision support  Reducing fraud and abuse  Better care coordination  Improving patient wellbeing Value Levers Technology HealthFore Technologies
  12. 12. • The industry is far behind other sectors in terms of adopting the latest technology and analytics • For privacy reasons, it can often be difficult to obtain access to data • There are very different costs to false- negatives and false-positives • Establishing common technical standards • Increasing confidence in safety and safe use of health IT • Developing an international communications structure • Stakeholder collaboration What are the risks? Technology
  13. 13. • A boon in wearable devices that track activity and biometric data • The evolution of electronic medical record systems • The creation an interoperability roadmap • Increasing privacy and security Future trends Technology
  14. 14. • Bianchard, T., (2017), Data Science and Predictive Analytics in Healthcare • Huffington Post, How Big Data Could Transform The Health Care Industry, (video) • Heusch, M., (2017), Using It or Losing It? The Case for Data Scientists Inside Health Care • Lebied, M., (2017), 9 Examples of Big Data Analytics in Healthcare That Can Save People • Montgomery, M., (2016), The Future Of Health Care Is In Data Analytics • Rao, V., (2015), Healthcare Data Analytics • Raghupathi, W. (2014), An Overview of Health Analytics Bibliography Next Steps
  15. 15. • What is the organization’s business model? • Why does the organization focus on data? • How is the Data Science team organized? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? • How does the organization use Data Science to propel growth Case Study Questions Technology