Mobile and Electronic Health: Anytime, Anywhere

Transcription

1 Mobile and Electronic Health: Anytime, Anywhere Peter Tilkemeier, MD, MMM, FACC, FAHA, FASNC Chair, Department of Medicine Greenville Health System Professor, University of South Carolina School of Medicine Greenville Professor, Clemson University School of Health Research

2 Disclosure No financial disclosures

3 Mobile and Electronic Health ehealth -health care practice supported by electronic processes or communication mhealth - using mobile communication devices, such as mobile phones, tablet computers and PDAs, for health services and information subset of ehealth

4 Patient Visits Synchronous Face to face with EHR evisit Asynchronous Data collection Data interpretation Data presentation

5 Applications Diagnostic Testing Population Health Genomics Targeting populations/individuals at risk Behavioral change/patient engagement Gen X Millenials Recruitment/participation in research trials Decision support Staying current

6 Diagnostic Testing Colormetrics Urinalysis Ophthalmology Dermatology Parkinson s detect changes in mobility Thyroid disease/malaria blood sample required Asthma/Atrial fibrillation detection Echocardiogram

7 Population Health Collecting big data Pattern recognition Early detection Communicable disease intervention/prevention Scaling response World health low cost

8 Genomics/At-risk Populations Association with low frequency events Variable expression Identifying populations/individuals at risk Statin responders Warfarin resistant Physician education Clinical decision support Phenotyping

9 Phenomapping Determines Event Risk J Am Coll Cardiol. 2015;66(13): doi: /j.jacc

10 Populations/Individuals at Risk Group visits for lower risk Intensification for higher risk Managing by exception Lowering cost of care delivery Improving quality of care delivered (outcomes)

11 Behavioral Change Patient Engagement Weight loss Little known regarding efficacy Greater diversity needed No comparative effectiveness data Need for real time monitoring/intervention Physical Activity No comparison to existing research based tools No mechanism for validation of apps Exergaming gaining interest

12 Behavioral Change Patient Engagement Smoking Cessation Phone apps are effective Variability in the response Limited data supporting efficacy Getting motivated Diabetes Special population needs Efficacy in long term outcomes unknown Expand Quality of Life data

13 Behavioral Change Patient Engagement Blood pressure control Identify most effective behavioral targets Adapt current knowledge to mhealth platforms Expand consumer base Dyslipidemia Lack large efficacy trials How to promote patient engagement Provider/patient interface development

14 Behavioral Change Patient Engagement Text messaging trial of lifestyle focused support program diet, activity, smoking Risk Factor Control Intervention LDL cholesterol (mg/dl) 84 79* BP control 54.9% 79.2%* BMI * Exercise frequency 22.5% 53.8%* Reduced smoking 55.9% 74.6%* *p < 0.05 JAMA. 2015;314(12): doi: /jama

15 Patient Generated Data

16 Patient Generated Data - Response Do not believe the data device error Do not trust the data input error Do not want to use the data Need to see the patient Trust but verify How do I get credit for the work? Changing compensation models It is great to have such an engaged patient!!

17 Recruitment/Participation in Research Trials Apple ResearchKit Large scale recruitment Minimize inter-site variability Promote diversity Utilize existing apps for gathering data

18 Staying Current 1 million Pub Med articles annually 0.1% is equivalent to 20 articles weekly Generational delay (17 years) in widespread implementation of proven therapies Aspirin in acute MI Leverage technology at point of care Personalized given knowledge from the EHR

19 Decision Support Outpatient clinical decision making Clinical questions arise once every 2 patients Answers pursued 50% of the time Answers found only 78% of the time 2/3 of time question never answered

20 Office Visit of the Future J Am Coll Cardiol. 2015;66(13): doi: /j.jacc Copyright The American College of Cardiology. All rights reserved.

21 Enabling Devices Medication Adherence Remote Biometric monitoring Heart rate Blood pressure Rhythm detection Bioimpedance Glucose monitoring Transcatheter pacing LVAD closed circuit

22 Electronic Rx Caps

23 Proteus

24 Example of a Wrist-Wearable Multiparametric Sensor An early prototype of the Quanttus device measures multiple physiological parameters, including continuous beat-to-beat blood pressure. It is currently being evaluated in clinical studies. J Am Coll Cardiol. 2015;66(13): doi: /j.jacc

25 Zio Patch 4 weeks of recording Integrates with App for symptom tracking Proven effective RCT Cryptogenic stroke Cost <$200 for patch

26 Medtronic Reveal Linq

27 CoVa ECG waveforms Respiratory rate Bioimpedance Thoracic impedance Heart rate Heart rate variability Future Cardiac output Stroke volume

28 Google Glucose Monitoring contact

29 Medtronic Micra Transcatheter Pacer

30 HeartWare MVAD

31 Clinical App Development Apple Store Android Weight loss Physical Activity/Exercise 72/ /120 Smoking Diabetes Mellitus Hypertension/BP 214/ /250 Cholesterol Medication Adherence

32 Approval Process Minimal number of randomized controlled trials of devices or apps Development of pathways for approval Clinical effectiveness/ease of use Special population applications Governmental/Insurers/payors Integration of patient generated data into the EHR

33 Future (not so distant) % of healthcare will have attempted hacks with 33% successful % of hospitals will develop comprehensive patient profiles allowing personalized medicine % will invest in technology we have discussed today % of health data will pass through the Cloud at some point % of healthcare data will be unprotected but needs to be protected given its value

34 Future Prediction of events to occur ICU biometric tracking Patient outcome tracking Physical activity in Parkinson s BP control Increasing depression scores Medication compliance

35 Challenges Reproducible data regarding efficacy Evaluation in more diverse populations Practice redesign Reimbursement Training required/competency Licensure Practice across state lines

36 Office Visit of Today J Am Coll Cardiol. 2015;66(13): doi: /j.jacc Copyright The American College of Cardiology. All rights reserved.

37 Thank You!

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