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Patient Identity and Digital Record Matching: A New Approach March 1, 2016 Tess Coody, CEO, Tenet Health Roderick Bell, CIO, Tenet Health

Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

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Page 1: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Patient Identity and Digital Record Matching: A New Approach

March 1, 2016

Tess Coody, CEO, Tenet Health

Roderick Bell, CIO, Tenet Health

Page 2: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Conflict of Interest

Tess Coody-Anders, CEO, and Roderick Bell, CIO have no real or apparent conflicts of interest to report.

Page 3: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Agenda

1. The Topic: An identity-centric, credential-based approach

2. Method of Analysis

3. The Problem: Mismatching, misidentification and fraud

4. Impact on Healthcare

5. Recommendations

Page 4: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Learning Objectives

• Learning Objective 1: Identify the negative impacts of the current

misidentification and mismatching problems in healthcare, such as

poor patient care, inconvenience for patients and providers, and

lack of patient safety

• Learning Objective 2: Evaluate the use of strong credentials for

fraud reduction based on their ability to strengthen transaction

processing and eliminate vulnerabilities in the healthcare payments

system

Page 5: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Learning Objectives

• Learning Objective 3: Initiate change in the way the U.S.

healthcare system identifies patients and manages care by

implementing an identity-centric, credential-based patient matching

and identity management model

• Learning Objective 4: Summarize the methods and solutions that

the healthcare provider in the session used to address their identity

management issues

• Learning Objective 5: Appraise the success of the implementation

case study detailed in the session and the extent to which identity

management challenges were overcome

Page 6: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

http://www.himss.org/ValueSuite

Satisfaction: The identity-centric, credential-based model is more

convenient for patients, simplifies admissions, reduces the risk of errors,

and saves patients time and money by avoiding redundant tests and exams

S

T Treatment: Improved patient identification will reduce redundant tests

and adverse events caused by misidentification

E Electronic Information/Data: Improved patient identification will reduce

redundant tests and adverse events caused by misidentification

P Prevention and Patient Education: Smart credentials can provide

simplified and more secure access to patient records, increasing

engagement by patients

S Savings: Smart credentials can help health care organizations save

billions lost each year due to fraud and medical identity theft

Page 7: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Saving Lives and Money: Interoperable, Digital Identity

• Identity-centric focuses healthcare systems and

processes around the accurate, reliable and repeatable

identification of the patient’s identity

• Credential-based means that a trusted digital device,

most likely an identity card or smart phone is used by

the customer to verify his or her identity to healthcare

providers

Page 8: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Creating an Interoperable Digital Identity System

Deterministic Interoperable

ID Tokens

Source: LifeMed ID

Page 9: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Why Does Identity Matter?

• Doyle Wesley Coody Jr. and Doyle Wesley Coody, III –

two people, one MRN

012345

Page 10: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Method Analysis

• Cross-Industry Collaboration

– Six years of analysis by collaborative of payers, healthcare and technology providers through Smart Card Alliance

• Compare and Contrast

– Healthcare versus Financial Services, Federal Government best practices

• Insights from Workgroups

– WEDI, Identity Ecosystem Steering Group, HIMSS Identity Management

Page 11: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

The Problem

• Awareness of the scope and scale of problem

• Technology and functional silos across disparate

systems

• Wait-and-see attitudes

• Noisy healthcare environment with many priorities

Page 12: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Impact on Healthcare: Patient Safety

• 12 percent of U.S. health records mismatched1

• 19 percent of CIOs report adverse events resulting from

misidentification2

• 198K deaths annually (2010)3

• 10 out of 17 deaths due to “wrong patient” errors4

S T E P S

Sources

1. & 2. According to Michelle O’Connor, director identity and information governance, QuadraMed

3. & 4. Death Statistics, according to the IOM

Page 13: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Impact on Healthcare: Fraud

One SSN on the

Black Market:

$0.43

One healthcare

record on the

Black Market:

$50 S T E P S

Page 14: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Impact on Healthcare: Fraud and Waste

• $77 billion in

Medicare/Medicaid fraud and

improper payments

• $13,450 out of pocket costs to

victims

• Duplicate records cost average

hospital $500k to $2.5M

Source: Medical Identity Fraud Alliance (MIFA), 2014

S S T E P S

Page 15: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Impact on Healthcare: Patients

• 45 percent of victims said the

incidents damaged their

reputation

• 19 percent said it cost them

career opportunities

• 3 percent said it actually caused

them to lose their jobs Source: Medical Identity Fraud Alliance (MIFA), 2014

S T E P S

Page 16: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Impact on Healthcare: Patient Experience and Education

• Automated registration experience

• Increased privacy and safety

• Assurance that their doctor is accessing

their correct information

• Prevents redundant paperwork and

testing

• Easy auto pay

• Easy access to personal medical record

S T E P S

Page 17: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Breaking Down the Silos

Automate Workflow

using Provider’s

Current Software

Validate Patient, Token

Photo, PIN, Biometric 17

Medicare, Medicaid,

CHIP, Debit, HSA Patient ID and Data Exchange Portal™

Bridging EMS

with Patient

Data and ER

Source: LifeMed ID

Page 18: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Big Data &

Analytics

Home Health

Government Payers-

Insurance

Hospital

Clinic/Physician

Education

Interoperable ID Tokens and Data Shared Between

Disparate Groups

Financial iServices

iRetail

Smarter

Retail

Smarter &

Safer

Healthcare

Smarter &

Safer Cities Social

Business

First Responders

Smarter Communities

Source: LifeMed ID and IBM

Page 19: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

http://www.himss.org/ValueSuite

The industry is ready, now

S

T

E

P

S

The utilization of a unique identifier is

realized in all subject matters within

STEPS™.

Operational and clinical change starts with:

1. Positive patient identification

2. Accurate record matching

3. Workflow automation

Case study: Avg. admission time

was reduced from 4 minutes to

less than 1 minute

Assures a minimum

accomplishment of an

LOA of 3

Case study: Reduced

duplicate errors from

7% to less than 1%

White paper: Enabled the hospital to

work with community and local retailers

to help change overall health

The operating budget impacted by

utilizing authoritative digital identity:

$1,837,917.33

Page 20: Patient Identity and Digital Record Matching: A New Approach · 2016. 2. 19. · Big Data &Smarter & AnalyticsSafer Cities Home HealthDisparate Groups Government Payers- Insurance

Questions?

Tess Coody-Anders

[email protected]

210-884-8060

Roderick Bell

[email protected]

505-659-8947

Smart Card Alliance

[email protected]

800-556-6828