Data Across the Continuum of...

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1 Data Across the Continuum of Care Nursing Symposium, February 11, 2019 Becky Fox, MSN, RN-BC, Chief Nursing Informatics Officer Stephanie H. McIntyre, MBA, RN-BC, CHPQ, Assistant Vice President, Information & Analytics Services

Transcript of Data Across the Continuum of...

Page 1: Data Across the Continuum of Care365.himss.org/sites/himss365/files/365/handouts/552515071/handout-NI5.pdfAmb. Care Mgmt: Hgb A1C (n=961) Compare lab values in patient cohort 1 month

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Data Across the Continuum of Care

Nursing Symposium, February 11, 2019

Becky Fox, MSN, RN-BC, Chief Nursing Informatics Officer

Stephanie H. McIntyre, MBA, RN-BC, CHPQ, Assistant Vice President, Information & Analytics Services

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Becky Guess Fox and Stephanie H. McIntyre have no real or apparent conflicts of interest to report.

Conflict of Interest

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• Data challenges across the continuum

– Current healthcare landscape and challenges in US

– How has data landscape has changed?

– Data across the continuum drives value

– Alignment and practical applications

– Data challenges

• Define how data can be used to change individual behavior

– Stakeholders

– Comprehensive Assessment

– Examples

– Suggested Steps

Agenda

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• Discuss how nurse informaticists can ensure proper systems translation and communication

– Definition

– How?

• Identify essential organizational players and their vital roles in transitional care

– Key players

– Thinking differently

• Provide key takeaways in identifying source data for specific transitions of care

– Data sources

– Care Management across the continuum

• Lessons learned from our organization

Agenda (continued)

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• Identify potential challenges with data flow across the continuum

• Define ways data can be used to change individual behavior

• Discuss how nurse informaticists can ensure proper systems translation and communication

• Identify essential organizational players and their vital roles in transitional care

• Provide key takeaways in identifying source data for specific transitions of care

Learning Objectives

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Data challenges across the continuum

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Healthcare Challenges in the US 250,000 deaths per year due to medical error

Comprises 18% of GDP . . . and increasing

US quality ranks low when compared to other developed countries

$3.06 trillion spent in 2014*; growing at rate of 5.8 percent**

“Waste” = $765 Billion (30% of total):

– $210B – unnecessary services

– $190B – excessive administrative costs

– $130B inefficiently delivered services

– $105B prices too high

– $75B fraud

– $55B – missed prevention opportunities

Over 54 Million Enrolled in Medicare^; 78 Million by 2030 (last year of baby boomer eligibility)

Sources: *Office of the Actuary at the Centers for Medicare and Medicaid Services; **National Health Expenditure Projections, CMS; ^Kaiser Family Foundation;

^^Facts on Medicare Spending, Kaiser Family Foundation; ^^^2013 Actuarial Report, Department of Health and Human Services

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How has the data landscape changed?

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Data Across the Continuum

LONGITUDINAL PERFORMANCE MANAGEMENT

(Population Health Management)

PayersPhysician

OrgsCINs ACOs Employers

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Data across the continuum

Connect the

continuum

Empower patients,

care teams,

and organizations

Facilitateknowledge-driven

care

and continuous

learning

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Outpatien

t

Ambulatory

Home

HealthAcute

CareSNF

Lab

Patient

Epic Cerner EMR1 EMR 2 EMR 3 EMR 4 EMR 5

Data Repository & Master Patient Index

Population Health Platform

Care

Transitions

Care

Gaps/Healt

h

Disparities

Risk

Stratificatio

n

Chronic Care

ManagementWellness

Support

Alignment

of Social

Services

Disease

Managemen

t

ED

Utilization

Need to focus alignment of data that will enable us to:

• Assist in strategic decision-making

• Risk stratify patients and populations

• Analyze potential opportunities for care improvement and cost reduction

• Assess gaps in care delivered

Alignment of Data and Information

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What is the practical application?

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Interoperability

•Variability in systems

•Expense to aggregate and normalize

•Timeliness

Security and Privacy

•Risk

•Data Use Agreements

•May limit access

Applicability

•Turning data into actionable information

•Embedding meaningful data into workflow

•Emerging patient engagement technologies

Data Quality

•Provider Attribution

•Data Validation

•Patient Generated Data

•Data Governance

Culture

•Perfect vs Good

•Overengineering

•Crawl before Walking

Data Challenges

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Using Data to Change Behavior

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Stakeholders and things that influence

• All stakeholders benefit when patients reach their optimum level of wellness, self-care management, and functional capability. These stakeholders include the patients, their support systems, and the healthcare delivery system providers of care, the employers, and payors.

Ability to Self-Care

Developmental State

Resource availability

Health State

Family system factors

Sociocultural orientation

Age

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Comprehensive AssessmentMedication Adherence Assessment and Evaluation

Environment, Resources and Services

Financial Health

Fitness Habits

DME/Functional Evaluation

Skilled Services Evaluation

Systems Evaluation

Barriers to Care

PHQ-9 and Referral to BHI if warranted

Readiness to Change

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Summary Diabetic Cohort Study

n=2599, 30% reduction in overall visits and 28% reduction

in overall billed charges for a total of $6,288,550 reduction

Pre and Post 180 days enrollment

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Amb. Care Mgmt: Hgb A1C (n=961)

Compare lab values in patient cohort 1 month prior to ACM program enrollment and 1 month prior to program graduation date

Average 1

month prior to

program

enrollment:

Average

HbA1C: 9.50

Median HbA1C:

9.10

Average 1

month prior to

program

graduation:

Average

HbA1C: 7.04

Median HbA1C:

7.00

Statistically significant reduction in HbA1C is found pre and

post ACM program enrollment at group Level

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ED High Utilizer Cohort

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Ability to view preventable ED visit volume by hospital

Key metrics including number of

visits and net margin by age

bands

Preventable ED visit detail by practice

attribution

Filter visit volume by condition

Building Visibility

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Building Visibility

Filters for preventable ED category and

primary diagnosis

Ability to view practice-level volumes of

preventable ED visits

Graphic illustration of visit type by time

of day

Patient density analysis by distance

to nearest ED

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Use Data to Build Patient Population ListIdentifying Markers in the Data

• 30 or more visits within one EMR

• 158 patients identified

• 7054 visits

• $22,022,370 charges

Payor Mix

• 2003 Self Pay

• 2084 Medicaid

• 918 Managed Care

• 464 Medicare Advantage

• 1560 Medicare

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Craft TacticsGame Plan

• Contact patients and enroll

within appropriate care

management programs

– Identify barriers to care

– Enroll in applicable programs

(BH, substance abuse, housing,

etc. )

– Keep ongoing contact

– 1st Data, YTD Feb

• Monthly roll call

– March 24th

Goal: Reduce Billed Charges

50%

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Work as a TeamRunning Man Intervention

• Data Analysis

• Who

• Payor

• Diagnosis

• Time of Day

• Then

– Patient Interview

– Structured data collection

• interventions

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Work as a Team

ED Bouncing Ball Icon

The icon will display on the FirstNet tracking

board in the Events column as soon as the

patient is registered to the ED.

At the same time, a page/email will also be sent

to the CCM distribution list for each facility.

The logic behind the icon and page is looking for

at least one Inpatient visit that was discharged

within 30 days of the current ED visit’s arrival

time.

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See Results

• Based on the incredible work by the IAS Team and the Care Management Team, there has been a 46% overall reduction in ED admissions in three months.

• Existing resources at CHS are being leveraged to help this group of patients. The Transitions Clinic is one example of how repurposing could keep the ED admissions down and help us hit our system goals.

• Systems are now in place to catch these patients that may have fallen through the cracks previously due to bouncing around locations, lower charges, lower numbers of inpatient admissions, or lack of complex chronic or rising risk data screening.

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Nurse Informaticists can ensure proper translation and communication

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“The specialty that integrates nursing science with multiple information management and analytical sciences to identify,

define, manage, and communicate data, information, knowledge, and wisdom in nursing practice.

NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making

in all roles and settings to achieve desired outcomes.

This support is accomplished through the use of information structures, information processes, and information technology.”

https://www.himss.org/what-nursing-informatics

Nursing Informaticists

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• Validating data quality from external sources and ensure correct matching

• Understand current and future state workflow

• Operationalize how clinicians will use

• Support teams with change management through the transition

• Leverage Clinical Decision Support

– At the right time

– Meaningful alert

– Breakdown the data

– Example

• Understand business, financial, strategic impact to the organization

• Communicating differently

How can Nursing Informaticists contribute?

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• Registry Development

– Understand clinical processes

– Identify care gaps/workflow challenge

– Validate populations and measures

– Support providers around attribution

Example

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Essential Organizational Players and their Vital Roles in Transitional Care

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• Everyone plays (or will play) a role in Population Health!

• Key will be the requirement of a cultural shift and not “the way

we’ve always done it”

• Requires understanding of the bigger picture and the bigger

contribution

Essential Organization Players

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Think di erentlyff• Sharing the EMR or view (not just sending pt with a paper packet)

• Extending your care coordination technologies (Seeing care that occurs outside of your organizational walls, e.g. Patient Ping)

• Creating bi-directional feedback loops (Connecting with outside resources, e.g.Connecting services for a patient with Aunt Bertha)

• Ensuring a feedback loop

• Connecting clinical leaders at both locations

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• Care Management / Home Health (including from the payors) that can assist in smoothing the processes – has to extend beyond the traditional players

– E.g. aunt Bertha examples – faith based, don’t just be stuck in nurse in acute and nurse in SNF

– Social determinants of health play a part in this and understanding communities and the societal support

Their Vital Roles in Transitional Care

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Alignment of Assets and Stakeholders

Physicians &

Providers

Post Acute Patients

Leaders &

Associates

Acute Care

Facilities

Payers &

Employers

Ambulatory

We need to integrate all aspects of care to improve quality,

reduce costs and improve outcomes for the patients we serve.

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Key Takeaways in Identifying Source Data for Specific Transitions of Care

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Data across the continuum

Connect the

continuum

Empowerpatients, care

teams,

and organizations

Facilitateknowledge-driven

care

and continuous

learning

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And many more…

• Faith based organizations

• Community resource organizations

• Patient self-management solutions

• Other potential business / health/ pharma / vendors will continue to morph in this space

BE PREPARED to innovate or think outside the box!

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Cross Continuum Care Management

Mu

lti-

Dis

cip

linar

y Te

am

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• Highest ED utilizer in Atrium System for at least past 3 years (over 1500 service/visits within Atrium Health

• Jan – April 2018 (120 calendar days), Joe has had 104 ED visits

• Other 16 days spent inpatient or observation

• ED, Inpatient, and Observation Facility Charges from 2015-2017 are over: $1,570,900

• YTD 2018 charges are $366,125

Joe’s Story

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Meet Joe

• Joe comes to ED because of an

overwhelming fear he will die of

numerous different medical

ailments.

• He lives in a car and moves

between parking decks (CMC-

Main and Union) to have quick

access to the ED.

• Joe notes that the only thing that

helps him feel normal is coming to

the ED daily and having a doctor

reassure him that he will be fine.

• PTSD

• Overwhelming anxiety

• Hypochondriasis

• Major Depressive

Disorder

• Alcohol Use Disorder

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New concept: Population Health version of Medical Ground Rounds

Launched on May 18, 2018

Multi-Disciplinary team meets monthly and present complex unresolved cases

Through critical thinking and collaboration an action plan is designed

Focused efforts to solve for self-imposed constraints and for absence of process

How do we know about Joe?

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What did we do for Joe?

Care Team MeetingPARC Staffing

ScheduledObtained Housing (and sold his car)

Daily Behavioral Health Therapy

Assigned to Primary Care

Provider

Cell Phone, Food Stamps,

Medicaid, SSI

Daily Community Paramedicine

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Update on Joe

“Yeah things are moving in the right

direction. Finely, I feel like a human again.”

Only 3 ED visits since Joe secured housing on June 12

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• Understand your process drivers for defining the data you need

and don’t try to “boil the ocean”

• Leverage informaticists in the data aggregation and validation

processes

• Consider what each stakeholder needs to know and that the

information you share is actionable, or connected to automatic

actions

• Think differently

• Consider the patient holistically

• Share data to support the patient’s specific needs

Lessons Learned and Key Take Aways from Our Organization

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Contact information:

Becky G. Fox, MSN, RN-BC

Chief Nursing Informatics Officer

[email protected]

Linkedin: Becky Fox

Stephanie H. McIntyre, MBA, RN-BC, CRHQ

Assistant Vice President, Information and Analytics Services

[email protected]

Linkedin: Stephanie H. McIntyre

Please remember to complete the online session evaluation

Questions