CHIA PRESENTATION HANDOUT

Transcription

1 5055 E. McKinley Ave, Fresno CA Tel: (559) CHIA PRESENTATION HANDOUT 2018 CHIA CONVENTION & EXHIBIT SAN DIEGO, CA A Proposed National Solution for Patient Matching Presented by: Joaquim Neto Presented on: Wednesday, June 06, 2018 California Health Information Association, AHIMA Affiliate

2 A Proposed National Solution for Patient Matching 2017 California Health 2018 Information California Health Association Information Association Disclaimer This material is designed and provided to communicate information in an educational format and manner. The presenter is not providing or offering legal advice but, rather, practical and useful information. Every reasonable effort has been taken to ensure that the educational information provided is accurate and useful. Applying best practice solutions and achieving results will vary in each hospital/facility and clinical situation. 1

3 About the Presenter Joaquim Neto VP Healthcare, Verato Decades of experience in information management and master data management (MDM) technologies Particular expertise in healthcare and in Master Patient Index (MPI) and Health Information Exchange (HIE) solutions Has consulted and delivered solutions for healthcare providers and payers of all types from Fortune 500 health systems to community hospitals Previously worked at Initiate Systems and IBM. Agenda Review why conventional patient matching technologies can never scale to become a national solution Explain Referential Matching and why it is much more accurate than conventional matching technologies Discuss how a cloud-based Referential Matching service can act as a national patient matching solution Show how this cloud-based Referential Matching service can immediately be used to improve patient matching at providers and HIEs 2

4 Patient matching has become 10x more challenging in the past 10 years 10 years ago A few data sources Today Many data sources + info exchange + analytics Interoperability / Information Exchange EHR Lab Reg EHR / EMPI EHR Rev Patient Engagement PHR apps Pop Health EHR Lab Reg EHR / EMPI EHR CMS Rev Care Mgmt Patient portals Advanced Analytics Consolidations / Mergers Duplicate rates are rising yet many in the industry remain in denial 10 years ago A few data sources Today Many data sources + info exchange + analytics Average Duplicate Rate: 8-12% 1 Average Duplicate Rate: 20% 2 We re fine! Everything s fine! We don t have a problem! HELP!!! We re fine! Everything s fine! We don t have a problem! HELP!!! HIM staff HIM staff 1. AHIMA, CHIME,

5 Everything is not fine, we do have a problem, and someone needs to fix it Who What 5 senators penned letter to GAO urging national patient matching strategy Verato s involvement We met with Senate and House committees to educate about Referential Matching Researching approaches for national patient matching solutions Trusted Exchange Framework Common Agreement (TEFCA) National Patient ID Challenge We are championing the use of Referential Matching as a nationwide approach We recommended a nationwide Referential Matching resource We entered the challenge with Referential Matching as solution Patient demographic data is notoriously error prone and constantly changing Name 2018 Birthdate Address 4

6 Patient demographic data is notoriously error prone and constantly changing Errors Spelling error Transcription error Typing error Homonym error 2018 Patient demographic data is notoriously error prone and constantly changing Errors Spelling error Transcription error Typing error Homonym error 2018 Ambiguities Jr/Sr overlap Twins Cultural naming 5

7 Patient demographic data is notoriously error prone and constantly changing Errors Spelling error Transcription error Typing error Homonym error 2018 Incompleteness Missing data Default entries Sparse data Ambiguities Jr/Sr overlap Twins Cultural naming Patient demographic data is notoriously error prone and constantly changing Errors Spelling error Transcription error Typing error Homonym error Incompleteness Missing data Default entries Sparse data Ambiguities Jr/Sr overlap Twins Cultural naming Changes Name change Address change Phone change change 6

8 Patient demographic data is notoriously error prone and constantly changing 30% of patient demographic data is out-of-date, incorrect, or incomplete. Errors Spelling error Transcription error Typing error Homonym error EHR or EMPI Incompleteness Missing data Default entries Sparse data Ambiguities Jr/Sr overlap Twins Cultural naming Changes Name change Address change Phone change change Current patient matching technologies are only as accurate as the underlying data EHR or EMPI No Match Jane Smith 123 Main St. 05/03/1990 Jane Jones 456 Elm Rd. 05/03/1990 7

9 Current technologies barely match well enough within a health system EITHER OR EHR or EMPI EHR or EMPI Data Quality Data Governance Algorithm Tuning Data Stewardship Periodic cleanups 20% duplicate rate % duplicate rate 1. CHIME, National Patient ID Challenge Background Current technologies struggle enormously when matching between health systems EITHER OR Hospital A Hospital B Hospital A Hospital B HIE Algorithm Tuning Data Stewardship 50% error rate % duplicate rate 1. CHIME, National Patient ID Challenge Background 8

10 Current technologies can never match well enough for nationwide patient matching HIE 50% error rate HIE Hospital A Hospital B 50% error rate HIE Hospital C The foundation of Referential Matching is a comprehensive database of U.S. identities Data Sources CREDIT Reference Database TELCO GOV T / LEGAL Over 300M identities 30 years of historical data 60M updates per month 9

11 Each identity has a complete demographic profile including historical & incorrect data Data Sources Data Science Reference Database CREDIT TELCO GOV T / LEGAL Over 300M identities 30 years of historical data 60M updates per month Referential Matching is intrinsically much more accurate than traditional matching EHR / EMPI Matching ( Probabilistic ) Referential Matching A B A B No Match Match Reference Database EHR or EMPI 10

12 A cloud-based Referential Matching service can act as a national patient matching resource Reference Database Step 1: Make patient matching better at every provider, HIE, and QHIN Reference Database 1. Plug in to EHR / EMPI 2. Send toughest matches 3. Receive real-time results Stewardship tasks???? Hospital A???????? HIE???????? QHIN???? 11

13 Step 2: Leverage the Referential Matching service to enable identity interoperability 1. Plug in to EHR / EMPI 2. Index patient population to Reference Identities 3. Instantly gain ability to discover common patients with other organizations Reference Database Hospital A HIE QHIN 50% 98% 50% 98% Step 3: Leverage the Referential Matching service to cross-reference other forms of ID 1. Plug in to EHR/EMPI 2. Associate other forms of ID with Reference Identities 3. Enable identity interoperability across organizations with different forms of ID Reference Database Palm Scan in DL# out Hospital A Hospital B Hospital C Demographics Palm Scan Demographics Iris Scan Demographics Drivers License Number 12

14 Future Possibility 1: Referential Matching service enables collaborative stewardship 1. Plug in to EHR/EMPI 2. Data stewards update the Reference Database to the benefit of all participating organizations Reference Database Address Update New Child Update Hospital A Hospital B Hospital C Data Stewards Data Stewards Data Stewards Future Possibility 2: Referential Matching service enables patient-managed identity Reference Database 1. Plug in to EHR/EMPI 2. Patients update and manage their own demographic data at registration Hospital A Hospital B Hospital C 13

15 The cloud-based Referential Matching service can be composed of multiple vendors National Patient Matching Resource Reference Database A Reference Database B Reference Database C Match coordination Match coordination Hospital A HIE QHIN Summary Conventional patient matching technologies can never scale to become a national solution Referential Matching is much more accurate than conventional matching technologies A cloud-based Referential Matching service can act as a national patient matching solution This cloud-based Referential Matching service can immediately be used to improve patient matching at providers and HIEs 14

16 Question & Answer 15