HIMSS LTPAC Roundtable Series

Transcription

1 HIMSS LTPAC Rundtable Series Care Crdinatin and Analytics: Hspitals and LTPAC Data-Driven Partnerships December 13, 2016

2 Gregry L. Alexander, PhD Prfessr Sinclair Schl f Nursing University f Missuri Jhn F. Derr, RPh. CEO JD & Assciates Enterprises, Inc. Lrren Pettit, MS, MBA Vice President, HIS & Research HIMSS Nrth America alexanderg@missuri.edu jfderr@mac.cm lpettit@himss.rg

3 LTPAC ROUNDTABLE: Series T prvide LTPAC IT leaders (prviders, vendrs and academia) with a frum fr infrming HIMSS Nrth America (HNA) n ways the HNA sciety can help; advance the adptin and use f health infrmatin technlgies within LTPAC prvider rganizatins, and advcate LTPAC s IT needs and cncerns t external healthcare IT rganizatins (e.g. acute-care prviders; HIT vendrs; plicymakers).

4 LTPAC ROUNDTABLE: Calendar /13/2016 Engaging LTPAC Prviders in Hspital Care Crdinatin Effrts: The Why and Hw 10/11/2016 Care Crdinatin and Persn-Centric Lngitudinal Care 11/15/2016 The Challenges f Care Crdinatin Invlving Behaviral Health data: A Panel Discussin 12/13/2016 Care Crdinatin and Analytics: Hspitals and LTPAC Data-Driven Partnerships /10/2017 Care Crdinatin and Transitins f Care 2/14/2017 Preview f HIMSS17 and LTPAC/Care Crdinatin activities 3/14/2017 Critical Analysis f Care Crdinatin in Different LTPAC Settings 4/11/2017 Care Crdinatin and Behaviral Health Services 5/9/2017 Care Crdinatin and Cnnected Health 6/13/2017 Preview f LTPAC HIT Summit

5 LTPAC ROUNDTABLE: Agenda REVIEW: Nvember Survey Findings Lrren Pettit, VP, HIS, HIMSS HIMSS: Clinical and Business Intelligence Shelley Price, Directr, Payer and Life Sciences, Health Infrmatin Systems Care Crdinatin and Analytics: Hspitals and LTPAC Data-Driven Partnerships Steven Sctt, Chief Executive Officer, PintRight, Inc.

6 Yur Turn Hw cnfident are yu in the healthcare cmmunity s ability t securely share behaviral health data? Nt at all Slightly Smewhat Mderately Extremely cnfident cnfident cnfident cnfident cnfident

7 Hw cnfident are yu in the healthcare cmmunity s ability t securely share behaviral health data? Extremely cnfident 5.00 Mderately cnfident Smewhat cnfident 3.00 Slightly cnfident 2.00 Nt at all cnfident 1.00 All Respndents (N = 54)

8 Hw cnfident are yu in the healthcare cmmunity s ability t securely share behaviral health data? Extremely cnfident 13.0% Mderately cnfident 18.5% Smewhat cnfident 50.0% Slightly cnfident 11.1% Nt at all cnfident 7.4% 0.0% 25.0% 50.0% 75.0% 100.0% All Respndents (N = 54)

9 Hw cnfident are yu in the healthcare cmmunity s ability t securely share behaviral health data? Extremely cnfident 5.00 Mderately cnfident Smewhat cnfident Slightly cnfident 2.00 Nt at all cnfident 1.00 Prvider (N = 14) Vendr (N = 29)

10 Hw cnfident are yu in the healthcare cmmunity s ability t securely share behaviral health data? Extremely cnfident Mderately cnfident 7.1% 7.1% 17.2% 20.7% Smewhat cnfident 48.3% 57.1% Slightly cnfident Nt at all cnfident 0.0% 13.8% 14.3% 14.3% 0.0% 25.0% 50.0% 75.0% 100.0% Vendr (N = 29) Prvider (N = 14)

11 December s Yur Turn +

12 LTPAC ROUNDTABLE: Agenda REVIEW: Nvember Survey Findings Lrren Pettit, VP, HIS, HIMSS HIMSS: Clinical and Business Intelligence Shelley Price, Directr, Payer and Life Sciences, Health Infrmatin Systems Care Crdinatin and Analytics: Hspitals and LTPAC Data-Driven Partnerships Steven Sctt, Chief Executive Officer, PintRight, Inc.

13 HIMSS: Clinical and Business Intelligence Shelley Price Directr, Payer and Life Sciences, Health Infrmatin Systems HIMSS 1. Why Clinical & Business Intelligence at HIMSS 2. What is C&BI at HIMSS and ur fferings 3. HIMSS Analytics AMAM 4. HIMSS17 and C&BI

14 HIMSS Clinical & Business Intelligence: Why Purpse Clinical and business intelligence (C&BI) is the use and analysis f data captured in the healthcare setting t directly infrm decisin-making. It has the pwer t psitively impact patient care delivery, health utcmes and business peratins. As we start leveraging the pwer f C&BI, we will see imprvement in ur natin s entire healthcare system as well as in the medical and ecnmic wellness f patients. HIMSS C&BI effrts empwer its members with practical guidance and unbiased infrmatin n hw t harness these healthcare data. This knwledge ffers the ptential t infrm accuntable, high quality patient care and fuel unparalleled business insight all the while imprving an rganizatin s fiscal perfrmance. C&BI Cmmittee website:

15 HIMSS Clinical & Business Intelligence: What Missin Statement The HIMSS C&BI Cmmittee is respnsible fr creating thught leadership, resurces and tls t supprt the needs f ur members and the healthcare industry. Our areas f fcus address the rle f health infrmatin technlgies in supprting the effective management f C&BI. Tpics f fcus fr the Clinical & Business Intelligence Cmmittee and Task Frces include: Knwledge Management t supprt accuntable & quality care Case Management Analytics Infrmatics Decisin Supprt Research Data Warehusing/EDW Data Lifecycle Management Ppulatin Health

16 HIMSS C&BI: Cmmunity Our cmmunity supprts activities that prmte peer-t-peer netwrking, prblem slving, slutin sharing, and educatin. HIMSS C&BI Cmmunity webinars let members dig int pertinent subjects thrughut the year. Our nging web-based prgrams cver tpics such as: Data aggregatin and access thrugh EDW and clud slutins Data management, validatin, quality, and integratin Descriptive, retrspective, predictive, and prescriptive analytics Gvernance data and prgram Ppulatin health management including attributin and risk stratificatin Precisin medicine Predictive mdeling Reprting, dashbards, and visualizatin techniques Resurce management Skills sets and staffing

17 HIMSS C&BI: Task Frce CO-CHAIR: Marius Petruc, MD, MS President President Infrmatics Slutins, LLC CO-CHAIR: Michelle Vislsky, MBA, FACHE Zne Sales Executive East Regin f Canada & the United States Caradigm This grup creates resurces and tls that emply practical guidance and unbiased infrmatin t help healthcare rganizatins (prviders, hspitals, integrated delivery netwrks, health plans and ther stakehlders) use C&BI t harness, use and analyze data captured in the healthcare setting t execute ppulatin health management initiatives and imprve care and health utcmes. Meeting times: 3rd Tuesday f the mnth, 4:00-5:00pm ET

18 Tp 10 Critical C&BI Challenges Surce: C&BI Cmmittee Discussin and Survey Results FY15 Survey Vtes FY16 ranking Issue Best practices fr ppulatin health analytics, including advanced risk adjustment methdlgies (integratin f clinical and claims data t fuel risk mdels), and risk adjusted utilizatin rates 11 1 Integratin f CBI int clinical wrkflw (C&BI meets CDS) 8 4 Standards based data access frm EHRs (and ther surces) int analytics platfrms, including semantic nrmalizatin 7 3 Predictive Analytics - hw t develp datasets and mdels (bth clinical & business) 5 1 Triple Aim CQMs - Industry standard CQMs t measure quality f care fr at risk ppulatins. Enables manageable number f CQMs acrss all payers. 3 5 Bundled Payment Analytics - Fr bundled payment refrm t be successful, this must becme a cre cmpetency 3 6 Data gvernance & helping executives understand what C&BI can d fr them 3 6 Enabling patient activatin with CBI - What d patients really need? What C&BI truly drives patient activatin? What are the imprtant differences between prvider and patient needs? 3 6 Implementing what we learn frm ad-hc analysis and benchmarking (hw t target interventins based n data) 3 10 Precisin medicine and applied analytics fr translatinal research (e.g. cancer, genmics) - speed up bench t bedside 3 6

19 HIMSS C&BI: Tactics Gal 1: HIMSS leads effrts t ptimize health engagements and care utcmes using infrmatin technlgy Objective 1: HIMSS Nrth America s thught leadership is widely-sught, highly-credible and strategically impactful By the end f Q4 FY17, publish a review f the public health crisis in cmmunities and hw the use f health data, analytics, predicative analytics culd play a rle in mitigating the prblem resulting in three references / citatins. Gal 2: HIMSS ensures all stakehlders are engaged in the transfrmatin f health and healthcare thrugh the best use f IT and management systems Objective 2: HIMSS Nrth America s thught leadership attracts, and engages, key audiences Submit a HIMSS17 pre-cnference sympsium prpsal n clinical & business intelligence r ppulatin health. If accepted, success measured by n fewer than 75 paid registrants. If nt accepted, repurpse prpsal int 2 blgs, lunch n learns, r similar. Objective 4: HIMSS Nrth America s rbust prfessinal and career develpment prgram equips all stakehlders t realize the transfrmatinal impact f IT in the health sectr Publish a C&BI and/r Ppulatin Health specific Staffing and Skills Guide, Resulting in three references / citatins.

20 Adptin Mdel fr Analytics Maturity Mdel Overview Capability riented apprach (nt technlgy riented) Healthcare industry specific, internatinally applicable Leverages an 8 stage maturity mdel, like EMR Adptin Prescriptive Each stage has specific cmpliance gals Bullet pint descriptin f cmpliance requirements Clearly defined requirements Standard terminlgy Simple assessment utlines a clear path t analytics maturity HIMSS Analytics 2016

21 Stage 7 Stage 6 Stage 5 Stage 4 Stage 3 Stage 2 Stage 1 Stage 0 Mass Custmizatin f Care and Strng Strategic Alignment Brad and deep data resurces beynd traditinal EMR Patient specific prescriptive care supprting persnalized medicine Symbitic cupling f strategy and supprting analytics Advance Clinical, Operatinal, and Financial Analytics Building n analytics insights Leveraging predictive analytics effectively Cllectively addressing the ecnmics f care Driving imprved utcmes and cupled financial perfrmance Maximizing quality f care fr patients and ppulatins Build Descriptive Analytics Fundatin Analytics fundatin building Strng, reliable, cnsistent descriptive reprting capabilities Data gvernance fr clinical, business, and peratinal insights Standardized analytics techniques, crdinated talent dev. Trustwrthy pl f well runded data Limited infrastructure and skills N crdinated effrts t manage data Fcus n intuitin and experience based decisin making

22 HIMSS17 C&BI Pphealth Sympsium February 19 Planning wrkgrup members: Dave Butler, Phil Cartagena, Ty Faulkner, Tny Linares, Jhn Middletn, Amy Minnich, Ravi Narayanan, Chet Rbsn, Michelle Vislsky

23 HIMSS17 HIMSS Spt: C&BI event February 20 HIMSS17: Clinical & Business Intelligence: Getting Smart n the Tp Issues and Resurces t Help Yu Turn Data int Actin Where are yu n yur pathway t value using clinical and business intelligence? Are yu defining yur current state, needs, and gals fr yur C&BI prgram? Wrapping yur arms arund data and rganizatinal gvernance? Trying t figure ut hw t use and integrate yur data, pen data, and Big Data fr yur strategic gals? Endeavring t put it all in place and emply ppulatin health management tls and strategies t bring value t yur ppulatin and rganizatin? Or d yu have war stries and best practices t share? Then cme t this meetup with the C&BI Cmmittee and its grups t learn abut new HIMSS tls and resurces such as the ppulatin health management capabilities guide and discuss the Tp 10 key C&BI issues fr tday and tmrrw, all t help yu n yur jurney t Turn Data int Actin. HIMSS17 Mnday, February 20, :00-1:45pm

24 HIMSS17 PpHealth Receptin February 20 6:00-7:00 pm PCM Knwledge Center HIMSS17 C&BI Cmmunity Receptin February 21 6:00-7:00 pm C&BI Knwledge Center

25 LTPAC ROUNDTABLE: Agenda REVIEW: Nvember Survey Findings Lrren Pettit, VP, HIS, HIMSS HIMSS: Clinical and Business Intelligence Shelley Price, Directr, Payer and Life Sciences, Health Infrmatin Systems Care Crdinatin and Analytics: Hspitals and LTPAC Data-Driven Partnerships Steven Sctt, Chief Executive Officer, PintRight, Inc.

26 HIMSS: Clinical and Business Intelligence Steven Sctt Chief Executive Officer PintRight Hspitals and LTPAC Data-Driven Partnerships: Leveraging Analytics t Accelerate Transparency and Cllabratin

27 Sessin Overview Wh is PintRight and what d we d? What s happening in the industry that s driving the demand fr acute-ltpac stakehlder alignment? What LTPAC data is available tday? Hw are acute care prviders using LTPAC data tday?

28 Infrming LTPAC Stakehlders with Actinable Data 20 Years experience using data t imprve quality in LTPAC 45 Millin+ identifiable recrds in ur data set 2 NQF endrsed re-hspitalizatin metrics Actively mnitr and measure: 15,500+ Skilled Nursing Facilities 2,000+ SNFs utilize PintRight 200+ Acute care prviders utilize PintRight 2014 Expansin in the payer market Natinal Managed Medicaid Plan ACOs

29 Trends in Pst-Acute Care 73% f ttal variance in Medicare spend cmes frm PAC 70% f a health plan s highest cst members pass thrugh PAC each year 42% f Medicare beneficiaries are discharged t a PAC setting Readmissins cst the healthcare system $30-$40 billin annually Surce: Medicare Payment Advisry Cmmissin (2015). March Reprt t the Cngress: Medicare Payment Plicy. Washingtn, DC

30 Grwth in Patients Receiving Pst- Acute Care 4.5% The percent increase in Medicare beneficiaries being discharged frm a hspital t a pstacute setting frm

31 Impact f Shifting Payment Plicy n Care Transitins Partnerships between acute and pst-acute care prviders are critical t imprving care crdinatin. 95% 94% High-value pst-acute care netwrks will be a key area f fcus ver the next three years. Creating high-value pst-acute care netwrks will be the greatest challenge ver the next three years. Findings frm Premier s Ecnmic Outlk Spring 2016 C-Suite Survey

32 ACOs are nw Targeting Pst-Acute Care Surce: Leavitt Partners Landscape Survey Pineer ACOs credited PAC fr FY2014 financial success Banner Health saved $29 millin verall and earned nearly $19 millin in bnuses Mntefire ACO saved $13 millin verall and earned $8.4 millin in shared savings

33 What are the building blcks t a winning pst-acute strategy?

34 Knwledge Hidden in Plain Sight Minimum Data Set (MDS) Standardized clinical assessment required by CMS fr all Medicare and Medicaid residents at certified Skilled Nursing Facilities (all payer) Over 500 data pints that address the member s: physical, mental, scial functins, diagnses, symptms & treatments Submissin required by CMS n day 5, 14, 30, 60 and 90 (quarterly thereafter), as well as upn readmissin/return t SNF, and at the start, stp r change f therapy

35 Data Sets Driving Analytics Skilled Nursing Facilities (SNFs) CMS s health inspectin database Nursing hme characteristics and health deficiencies issued and data abut staffing and penalties The Minimum Data Set (MDS) Data fr quality measures cme frm the MDS Repsitry Medicare claims Medicare claims data is used t calculate certain utilizatinbased hme health quality measures Medicare.gv/NursingHmeCmpare Hme Health Agencies (HHAs) The Outcme and Assessment Infrmatin Set (OASIS) Hme health quality measures (bth utcme and prcess measures) Medicare claims Medicare claims data is used t calculate certain utilizatinbased hme health quality measures. Patient experience f care survey Hme Health Cnsumer Assessment f Healthcare Prviders and Systems (HHCAHPS) Medicare.gv/HmeHealthCmpare

36 Building a Narrw vs. Smart Netwrk NARROW NETWORKS Efficient Geared tward the stakehlder SMART NETWORKS Custmized t fit individual needs Data driven

37 Case Study: Netwrk Perfrmance Management EXECUTIVE SUMMARY A multi-hspital system based in the Mid-Atlantic frmed a SNF Cllabrative t create a high-perfrming pst-acute prvider netwrk fcused n lwering readmissins, imprving utcmes by creating a business intelligence architecture with the fllwing gals: Enhance patient utcmes while reducing the cst f care Transparent and standardized KPIs facilitating best practices acrss the cllabrative Prvide data-driven decisin supprt thrugh descriptive and predictive analytics

38 Transparency Thrugh Cntinuus Perfrmance Reprting

39 Re-hspitalizatin Rate fr the Cllabrative PintRight Pr30 Rehspitalizatin 20% 19% 18.6% 19.0% 19.1% 18.6% 18.7% 18.3% 18.2% 18.2% 18% 17% 16% 18.1% 18.5% 18.5% 18.1% 18.2% 17.8% 17.8% 17.8% 17.5% 17.0% 17.1% 16.8% 17.3% 17.0% 17.1% 16.8% 15% Sep-15 Oct-15 Nv-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Observed Adjusted Reference Rates (Adjusted) Natin 17.0% State 16.7% Cunty 17.9%

40 Discussin Steven Sctt Chief Executive Officer PintRight, Inc. Gregry L. Alexander, PhD Prfessr Sinclair Schl f Nursing University f Missuri alexanderg@missuri.edu Jhn F. Derr, RPh. CEO JD & Assciates Enterprises, Inc. jfderr@mac.cm

41 December s Yur Turn +

42 ANNOUNCEMENTS: Next LTPAC Rundtable TOPIC: Care Crdinatin and Transitins f Care pm CDT