Health Datapalooza 2013: Affiliates Apps Expo MEDgle

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Health Datapalooza IV: June 3rd-4th, 2013
Health Data Consortium Affiliates Apps Demos
Moderator: Sunnie Southern, Founder and Chief Executive Officer, Viable Synergy, LLC; Ohio Health Data Affiliate

MEDgle’s graph-based big health analytics engine and platform provides real-time diagnostic, predictive, and prescriptive analytics for individuals and populations.

Presenter: Ash Damle

Published in: Health & Medicine, Technology
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  • What is hyper-personalization?
  • What is hyper-personalization?
  • What is hyper-personalization?
  • What is hyper-personalization?
  • What is hyper-personalization?
  • Health Datapalooza 2013: Affiliates Apps Expo MEDgle

    1. 1. making elegant sense of the world’s health datato guide and scale care delivery
    2. 2. MEDgle is a tech company, addressing healthcare’s problem ofover $300 Billion of waste, inefficiency and inaccuracyspanning 4 Billion care decisions because Health Systemsand Payers cannot:access and apply the best medical science within existingand emerging workflows, hyper-personalized, and withscale for individuals and populations?(PWC, Gartner)2
    3. 3. To solve this problem we took inspiration fromand looked at the world not as individual elements butas rich complex inter-connected graphs3
    4. 4. diagnostic, predictive, and prescriptive analytics… built with data mining + machine learning + physician curation (20K+ hours)… and created a graph-based big health analytics platform providingData-minedknowledgeReviewedknowledgeExpertKnowledgeAPIfeedback loops&inductive learning4Refined withEHR DataSensor Data3M+ accessible with14M+ coming soon7M+ pagesof text, textbooks, journal articles100GB+of public datasets and databasesHealthdata.govFDA.govPubMedNextBioRxlist.comCDCHealthweb.orgNice.org.ukUptoDateCleveland ClinicMayo ClinicNIHWebMDDiscovery.comMedicineNetMedical Journals*Drugs.comMerckAdamPedbase.orgYahoo HealthEmedicineCecil 19th EditionDiff. Diagnosis in Internal MedicineWalter Siegenthaler 2007Primary Care Medicine Alan Gorolland many more3M+ patient records (Q2’13)7M-14M patient records (Q4’13)
    5. 5. And today, the Graph of Medicine already has150M+ Data Points40K+ Symptoms & Signs4K Diagnoses7K Procedures7K Medicationsacross ages, genders,durations, lifestyles5
    6. 6. … and examples of the graph-based analytics areIndividual AcuteAnalytics6Individual ChronicAnalyticsPopulation ChronicAnalyticsReal-time contextual infoat the point-of-care(diagnostic+prescriptive)Personalized HealthForecast(predictive+prescriptive)Population wide healthforecasts andprescriptive options
    7. 7. … built on a scale-out architecture to support web-scale growth!MEDgle Graph of Medicine and APIHadoop |Couchbase | ElasticSearchHealthStreamStoreHealthcareApps acrosscarecontinuumEHRtext data mining,supervised learning7
    8. 8. HealthSystems &RiskBearersPatientsHITVendorsdelivered throughApps & APIsThe Graph of Medicine and algorithms areAPI: machine to machine interface to simplify use – application programmers interfaceApp: web and mobile software for specific purposes8
    9. 9. which turbo-charges a number of actionable use case workflows todayNurse Call CenterTriage & AdviceSoftwareHome TriagePHR & PatientAppsEHREmergency/UC/MD OfficeCare ManagementClinical Research & InnovationQuality Reporting& Measurementdiagnostic,predictive,prescriptiveanalyticsdiagnostic,prescriptiveanalyticsdiagnosticanalytics predictiveanalyticsdiagnosticanalyticsPopulationHealth Mgmt
    10. 10. Contact: Ash Damle – ash@medgle.com – 617.283.0226and lets see a few in action!hyper-personalizedTriage [Kelly – a call-center nurse in Kentucky]Health Assessment [ Doug – a care manager in NJ]graph-poweredpopulation analytics [Mark – CMO at an emerging ACO]
    11. 11. Contact: Ash Damle – ash@medgle.com – 617.283.0226Get access to the graph-based big health analytics enginepowering hyper-personalized care @ scalehello@medgle.com
    12. 12. A walk through Fred’s Triage (an example of Iterative Diagnostic Analytics)Initial Predicted DifferentialDx(in the background)Round 1 of Emergency Qs(Prescriptive Analytics)2nd Round Predicted DDx(raw data + R1 answers )Round 2 of Emergency Qs(Prescriptive Analytics)Final ESI + Triage DDx(raw data + R1, R2 answers )Post Triage Prescriptive analytics:an example of personalized acute care optionsPost Triage Predictive analyticsan example of assessing a patients current healthFred: 36 yrs, male, current dx: hypertension (ICD9401), 210lbs, 5’9”, family hx: CAD, current complaint:cough 1 day
    13. 13. An example of MEDgle Acute Analytics for Fred (Part 1a)Acute Predictive analytics:an example of assessing a patients current healthAcute Prescriptive analytics:an example of personalized acute care optionsPredicted Differential Diagnoses, Triage level with negativeanswers to emergency Qs, est acute costs, and moreAll test scores < 2stars  may not beof value to orderAll test scores < 2 star  maynot be of value to orderAll test scores < 2 star  OTC forsymptom reliefFor a mild tomoderate coughfor only 1 daywith no otherpresentingsymptoms andnegative to allemergencyquestion, it maynot be necessaryfor Fred to comein immediately.Patient can comein if hissymptomsworsen.Fred: 36 yrs, male, current dx: hypertension (ICD9401), 210lbs, 5’9”, family hx: CAD, current complaint: cough for 1day13
    14. 14. An example of MEDgle Acute Analytics for Fred (Part 1b)Acute Predictive analytics:an example of assessing a patients current healthAcute Prescriptive analytics:an example of personalized acute care optionsFred: 36 yrs, male, current dx: hypertension (ICD9 401), 210lbs, 5’9”, familyhx: CAD, change that to having a cough and fever for 3 weeksNow, with a cough and fever for 3 weeks, the predicteddifferential diagnoses and their ordering are differentSome test > 2stars  would beof value to orderAgain, small shift in symptoms and duration results in differentdifferential diagnoses, estimated cost, labs, and more. MEDgle’sanalytics are highly personalized to the individual and situation!Due to the non-resolution ofsymptoms withinthe expectedtime, it issuggested thatFred come inwithin 24 hours.Note that thestandard deviationfor est. acute costis larger than inthe previousexample as moretests may beneeded.Some scores are >2 stars May make sense to considerSome scores are >2 stars May make sense to consider14
    15. 15. An example of MEDgle Chronic Analytics for Fred (Part 2a)Chronic Predictive analytics:an example of assessing a patient’s future healthChronic Prescriptive analytics:an e.g. of personalized chronic care options and preventativemeasuresFred: 36 yrs, male, current dx: hypertension (ICD9401), 210lbs, 5’9”, family hx: CAD, chronic analysis [HTN, BMI: 31(obese), fhx: CAD]Beyond generating a highly personalchronic risk profile, MEDglegenerates a health FICO score(FitScore), est. yearly cost, “healthage” ,and projects his health over thenext 10 to 15 years with his currentweight and with a weight loss of16lbs.Translating the predictiveanalytics into prescriptiveanalytics, MEDgle calculatesFred’s top improvementareas, and symptoms to watchout for, and more.Combining guidelines withprobabilistic analysis, MEDgleprovides nuanced monitoringcalendars and therapy optionsas a starting point for a highlypersonal care plan.These analytics can beaggregated over a population tounderstand what programswould be most impactful, whatcross-population data pointswould be most meaningful, andmore.15
    16. 16. An example of MEDgle Chronic Analytics for Fred (Part 2a-1)Forecast in detail comparing trajectories of Fred’s current weight (orange line) to a weight lossof 16 lbs (green line) with no other change in variablesFred: 36 yrs, male, current dx: hypertension (ICD9401), 210lbs, 5’9”, family hx: CAD, chronic analysis [HTN, BMI: 31(obese), fhx: CAD]16
    17. 17. An example of combining Acute & Chronic Analytics for Fred (Part 2d)Real-time Acute Predictive Analytics with a Chronic Predictive contextan example of continuous health risk assessment combining MEDgle’s Analytics Platform and sensorsFred: 36 yrs, male, current dx: hypertension (ICD9401), 210lbs, 5’9”, family hx: CAD, chronic analysis [HTN, BMI: 31(obese), fhx: CAD]MEDgle’s real-time Analytics Platform is able to synthesize incoming sensor datawith contextual EHR data to provide a continuous health risk assessment. If atsome point, a triage is indicated to make sure Fred is ok, his care provider teamcan be messaged or a nurse call center can reach out to him.Combining the information, in thebackground MEDgle is calculatingwhat underlying causes are relevantfor Fred’s specific background andsensor inputs.*1017
    18. 18. raw data: dx,cpt,andrxehr&claims for 200 people for 2011Predictive analytics: Prescriptive analytics:Est Health Cost/yr By ZipBy FitScore (Risk StrataAF)By FitScore (∑ Est. Cost/yr)Top population improvement areas to minimize future costsAn example of MEDgle Analytics for a PopulationHigh At Risk for DiagnosesTop 26 At Risk for Diagnoses: Automated Top At-Risk DiseaseRegistriesTop valued diagnostic monitoring tests to conduct togather key data to improve prediction accuracy18
    19. 19. raw data: ehr + claims for 10k people for 2009-2012 (Predictive Analytics)By FitScore (RAF)By FitScore (∑ Est. Cost/yr)An example of MEDgle Population Analytics (Part 2)Top 26 At Risk for Diagnoses: Automated Top At-Risk DiseaseRegistries
    20. 20. raw data: ehr + claims for 10k people for 2009-2012 (PrescriptiveAnalytics)Gaps of Care vsFitscoreAn example of MEDgle Population Analytics (Part 3)High At Risk Diagnosis vs OpportunityGaps of Care vs Age RangePrevious Diagnossvs Opportunity
    21. 21. Contact: Ash Damle – ash@medgle.com – 617.283.0226a graph-based big health analytics platform,enabling hyper-personalized care @ scale

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