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Using Data Analytics to Improve Clinical Performance

Using Data Analytics to Improve Clinical Performance. Yvonne M. Mounkhoune. Welcome!. Yvonne M. Mounkhoune, RN, BSN, MA Practice Management Consultant. Registered Nurse for 24 years Pediatrics neurosurgery, transplant, ICU, and home health

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Using Data Analytics to Improve Clinical Performance

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  1. Using Data Analytics to Improve Clinical Performance Yvonne M. Mounkhoune

  2. Welcome! Yvonne M. Mounkhoune, RN, BSN, MA Practice Management Consultant

  3. Registered Nurse for 24 years • Pediatrics neurosurgery, transplant, ICU, and home health • Nurse Manager in adult acute care, urgent care, rehab, primary care • Nurse Recruitment, Case Manager, Consultant About Your Speaker

  4. Agenda • What is data analytics • Data analytics in preventive medicine • Data analytics in chronic care management • Data analytics predicting health outcomes • Barriers to data analytics • Future of data analytics

  5. Quotable Quote? Doctors prescribe medicine of which they know little, To cure diseases of which they know less, In human beings of which they know nothing. • Francois-Marie Arouet (Voltaire)

  6. What is Data Analytics? • Process of examining data sets in order to draw conclusions about the information they contain. • Big Data Analytics • Health care specific

  7. Why Big Data? • Improve profits • Cut down on wasted overhead • Predict epidemics • Cure disease • Improve quality of life • Avoid preventable deaths

  8. Amount of Data • 500 Petabytes of health care data in 2012 • Expected growth in health care data to 25,000 Petabytes by 2020 • Peta- prefix denoting a factor of a million billion

  9. Big Data!!

  10. More Big Data • Guiding treatment delivery decisions • Pittsburgh Health Data Alliance • Apple and IBM partnership

  11. Personalized Medicine “[Genome science] will revolutionize the diagnosis, prevention, and treatment of most, if not all human diseases.” President Bill Clinton Combining behavioral, psychosocial, and biometric data with epidemiology and clinical medicine will ultimately result in enhanced reengineering of clinical pathways and truly personalized care.

  12. Personalized Medicine Stats 2006 - 6 personalized medicine drugs, treatments, and diagnostic products available 2016 - 132 available medicine drugs, treatments, and diagnostic products available 2014- 20% of meds approved by FDA were 2015 personalized medicines 2016 27% of meds approved by FDA were personalized

  13. The Future of Personalized Medicine • Transition from one-size-fits-all to medicines specifically linked to diagnostics • Payment systems must be flexible • Regulatory guidelines must adapt • Medical Schools must include personalized medicine in their curricula • Patient, education, interest, and demand are essential as well.

  14. Preventive Medicine • Population Health Management Health management activities Screenings Tests Vaccinations • Example: The Iowa Clinic • Adult vaccination

  15. Mental Health, for example… • Significant results yielded simply by asking a few questions. • Basic depression screening questionnaire • Primary Care perfectly positioned • Referral process and community resources • Telehealth or mHealth services

  16. Chronic Disease Management • Lab values • Surveillance imaging • Pharmacology • Follow up • Compliance

  17. Diabetes, for example…. • One of the most costly, common, and underdiagnosed chronic diseases in our nation • People suffer from avoidable complications • Patient engagement through technology • Easier patient-provider communication • Increased patient engagement, compliance, and health literacy

  18. Stratification and Risk Scores • Common disease trajectories • Algorithms • Running reports • Choosing interventions • Follow up

  19. Predictive Analytics • The physician’s brain versus software tools • Data set versus individual experience

  20. Predicting Risk • Predictive analytics helps providers assess patients’ risk of contracting disease or other conditions. • Individualize treatment regimens by identify patterns of types of patients likely to respond to a particular therapy. • Pinpoint treatments that sustain health • Identify individuals who may stop benefiting from a specific treatment regimen in a given time frame.

  21. Using Predictive Software • Carolinas HealthCare System (CHS) • Uses software from Predixion, a California-based software company • At discharge, allows nurses to customize clinical interventions based on patient’s predicted risk of readmission. • Lowered readmission rates by a third.

  22. Who Will Need Help, for example… • Majority of a patient’swell being is related to lifestyle choices and situations • We lack knowledge of patient’s “community vital signs” • Psychosocial assessment necessary

  23. Current Barriers to Data Analytics • Budget constraints • Lack of commitment from executive leaders • Concerns about new workflows • EHR headaches or interoperability issues • Resistance from clinicians related to time management and/or extra tasks

  24. Other Barriers to Data Analytics • Lack of data sharing • Connecting data sources • Need for a new value framework

  25. The Future of Data Analytics A New Value Framework Needed • Right living • Right care • Right provider • Right value • Right innovation

  26. Why now?

  27. Questions?

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