8/12/2016. Topics to be covered. What is Diabetes Data Smog. Disclosure to Participants

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1 Disclsure t Participants Ntice f Requirements Fr Successful Cmpletin Please refer t learning gals and bjectives Learners must attend the full activity and cmplete the evaluatin in rder t claim cntinuing educatin credit/hurs Cnflict f Interest (COI) and Financial Relatinship Disclsures: Erik Ott, Emplyee and Equity Hlder in InSpark Technlgies, Inc. Nn-Endrsement f Prducts: Accredited status des nt imply endrsement by AADE, ANCC, ACPE r CDR f any cmmercial prducts displayed in cnjunctin with this educatinal activity Off-Label Use: Participants will be ntified by speakers t any prduct used fr a purpse ther than fr which it was apprved by the Fd and Drug Administratin. Tpics t be cvered Help Clients Overcme Diabetes Data Smg: Perspectives frm a CEO with Diabetes What is Diabetes Data Smg? Specific diabetes data analysis challenges What technlgies can help cut thrugh the smg Criteria fr evaluating and using new diabetes data analysis technlgies Erik Ott President and C-Funder InSpark Technlgies, Inc. Charlttesville,VA PWD Type 1 fr 20 years 12 years at J&J heavy fcus n diabetes data analysis technlgies. 4 years at InSpark develping and cmmercializing diabetes data analysis technlgies. What is Diabetes Data Smg 25 billin bld glucse data pints cllected annually MOST tls present back all the data with little value-added insight MOST peple d nt have the time t analyze all this data MOST peple d nt have the clinical acumen t make sense f the data 1

2 But wait, desn t diabetes sftware slve this prblem? Q1: What d yu think f this diabetes sftware? I lve it. The sftware is visually stunning and clrful Q2: Hw d yu use sftware t extract insight? Well... extracting insight frm diabetes sftware is ften mre art than science The impact f diabetes data smg Wng et al study 1 1% A1c diff (p=0.03) Survey f 155 adults and 185 caregivers f children (T1D) Benefiting Rutine Reviewers Peple wh have ever dwnladed meter data Adults=31% Caregivers=56% Peple wh are Rutine Dwnladers (4+ times in last year) Adults=20% Caregivers=40% Peple wh are Rutine Reviewers (Reviewed data mst f the time when dwnladed) Adults=12% Caregivers=27% 1. Wng, JC et al. Diabetes Tech Ther Vl 17, Num 8, 2015 What kind f art are we talking abut? Data acquisitin issues? Causes f data dysintegratin Lack f meter dwnlading standardizatin Gaps in data due t multiple meters Date / time issues Lack f fd / medicatin / activity data The impressinist view Lgging data is... Hard. But hw des diabetes data analysis make yu feel? The Scream By Edvard Munch The awesme but elusive dialgger 2

3 Issues with diabetes data analysis Sftware User Interface Evlutin T much data being presented The lg bk The spt chart Lack f cntext What des a standard deviatin f 50 really mean? The sftware des nt answer imprtant clinical questins Cnventinal User has t PULL insight GLANCEABLE insights Mdern PUSH ntificatins CONTROL systems Unanswered Clinical Questins When am I at risk fr hypglycemia r severe hypglycemia? Where d I have persistent daily patterns that, if mitigated, will imprve my glycemic cntrl and quality f life? Where d I stand in terms f my verall risk f acute cmplicatins? Hw can I imprve my basal insulin prfile? What are my meal time blus issues? Sftware User Interface Evlutin Cnventinal User has t PULL insight Spt graphs/ pie charts Data lgs Averages by time f day GLANCEABLE insights Glycemic Reprt Cards Glucse trends Indicatrs f risk Mdern PUSH ntificatins Real-time Pattern Messaging Alerts abut risk f acute events Blus advisrs CONTROL systems Lw-glucse suspend Artificial Pancreas Sftware User Interface Evlutin Cnventinal Mdern But there is hpe... User has t PULL insight GLANCEABLE insights PUSH ntificatins CONTROL systems In Thery... MORE WORK DONE BY COMPUTER LESS WORK DONE BY YOU / PATIENT BETTER INSIGHT / CONTROL = BETTER OUTCOMES 3

4 Des Thery = Reality? Nt always... Different frms f DM insights can have significantly different levels f utility What is a gd TPMS alert? Accurate estimatin f tire pressure Even mre NB: Warning is predictive f danger Pssible Scenaris: Accidents are 3x mre likely after a tire pressure warning Accidents are 10x mre likely after a tire pressure warning Diabetes cntrl is like driving a car Des anyne Knw what this Means? Try t stay between the lines (i.e. highs and lws) If yu dn t yu can get in an accident (i.e. severe lws, ketacidsis) 4

5 They say it s predictive, s it must be useful, right? Eg: Gluc Savant predicts 80% f bld glucse highs. Why d we use HbA1c? IMPORTANT QUESTIONS What infrmatin did it need t predict 80%? Only fd, insulin, glucse, heart rate, insulin sensitivity, and BMI In what ppulatin? Diverse ppulatin r nly red-headed 14 year lds. Independent validatin r fitting t a specific dataset? Hw many times did it flag r warn? What is the psitive predictive value? Frequency f warnings /alerts is critically imprtant Scenari 1: Scenari 2: Predicts 50% f severe lws Makes a predictin 1 x per day n average Assume 100 severe lws and 2000 days f data 1 / 40 chance f a severe lw every warning (2.5% chance) Predicts 25% f severe lws Makes a predictin 1 time every 10 days n average Assume 100 severe lws and 2000 days f data 1 / 8 chance f a severe lw every warning (12.5% chance) The predictive acid test wrks acrss the spectrum f data analysis tls Cntrl systems Cnstantly trying t predict future glucse and curse crrect Alert systems A CGM shuld be able t accurately predict lws and highs Daily Patterns An identified pattern f highs shuld be predictive f future highs If uncrrected Measures f Diabetes Cntrl A measure f variability shuld be predictive f glucse excursins Receiver Operatr Characteristic Usually a gd balance Will predict a fractin f events but will hardly ever be wrng Very few alerts And messages Will predict mst events but als be wrng a lt Lts f alerts and messages 5

6 Other insight utility cnsideratins Educatin is imperative Make sure yu have the right tl fr the jb Caching and supprt can help Ultimately, clinical utcmes are the ultimate barmeter f utility But make sure it is efficacius AND peple will use it. The right tl fr the jb Frequency Of Readings A typical distibutin f glucse data And less abut Yur risk f lws Clinical Center (112.5 mg/dl) Standard Deviatin tells yu mre abut yur risk f Highs Which means Standard Deviatin gives yu an unbalanced view f variability 0 mg/dl 600 mg/dl BG Scale The right tl fr the jb Example measuring glycemic variability Knwing glycemic variability is imprtant, but hw d we measure it? Mst peple use Standard Deviatin Is Standard Deviatin the right tl fr the jb? Timeliness When yu receive insight ften determines... Hw actinable it is Whether yu will pay attentin at all The right tl fr the jb Frequency Of Readings Balanced distributin f data Standard Deviatin But glucse data assumes the data Is typically nt is nrmally (evenly) nrmally distributed distributed Persnalizatin Sme peple like lts f messages / alerts, sme dn t Push messages shuld have frequency / sensitivity and ff settings Persnal glucse targets Diet / Lifestyle cnsideratins 0 mg/dl Mean BG 600 Glucse Scale mg/dl 6

7 Lifestyle Obtrusiveness Actually, sme f us will nt be assimilated Summary what t lk fr Useable: Lk fr DM sftware interfaces that are Glanceable, Push Alerts, r Cntrl systems Useful: Predictive, right balance f alert frequency vs sensitivity, validated, the right tl fr the jb, timely feedback Fr everyne: Persnalized, discreet, lw hassle, affrdable Lifestyle Obtrusiveness Lts f devices Cmplexity and learning curve Lack f discretin Hassle Data entry Calibratin HCP interactin (srry) Smg busting enablers Pattern recgnitin Predictive algrithms Expert systems Machine learning Cntrl systems engineering These technlgies can help us mve alng the Glanceable / Push / Cntrl system spectrum Accessibility cncerns Affrdability Availability Lack f supprt tls Summary See thrugh the smg, and help yur patients be diabetes data rck stars 7