Innovation Spotlight - HIMSS20...1 Innovation Spotlight Session #NI2, February 19, 2017 Tanna...

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Innovation Spotlight Session #NI2, February 19, 2017

Tanna Nelson, MSN, RN-BC, CPHIMS, Texas Health Resources

Sally Okun, VP Advocacy, Policy & Patient Safety, PatientsLikeMe

Kimberly Ellis Krakowski, MSN, RN, CENP, CAHIMS, Associate Chief Nursing Information Officer, Inova Health System

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Conflict of Interest

Tanna Nelson, MSN, RN-BC

Kimberly Ellis Krakowski, MSN, RN, CENP, CAHIMS

Have no real or apparent conflicts of interest to report.

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Conflict of Interest

Sally Okun

Salary: PatientsLikeMe

Consulting Fees (e.g., advisory boards): Commonwealth Fund, PCORI

Ownership Interest (stocks, stock options or other ownership interest excluding diversified mutual

funds): PatientsLikeMe

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Learning Objectives

• Outline the reasons why applying innovation will bring value to an

organization

• Describe lessons learned when developing a new approach or application

• Discuss how to overcome challenges and realize benefits when developing

and implementing an innovative solution

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Predictive Analytics for Reducing Readmissions within 30 days of Discharge

Session #, February 19, 2017

Tanna Nelson, MSN, RN-BC, CPHIMS, Texas Health Resources

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Texas Health Resources - Organizational Background• Texas health resources is one of the largest faith-

based, nonprofit health care delivery systems in the united states and the largest in north Texas in terms of patients served.

• The system's primary service area consists of 16 counties in north central Texas, home to more than 6.8 million people.

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Texas Health Resources• 25 hospitals in North Texas

• 14 wholly owned hospitals

• 133,903 Inpatient Visits

• 1,238,392 Outpatient Encounters

• 469,309 ED Visits

• 89,452 Surgeries

• 27,200 Deliveries

• 5,500 Active Physicians

• 7,500 RN’s

• 22,000 Employees

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Business Model

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Texas Health Resources & Readmission Risks

• Used multiple ‘brand’ name readmission risk indicators for 3 years

– Not effective/efficient enough in targeted outreach

– Some tools proprietary and risk factors were unknown

• Gap in managing and reducing readmissions

– Limited resources

– Can’t reach every patient but need to reach the right patients

• Requested for more data that defined our unique population

• Formation of a Readmission Taskforce

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Latest Readmission Risk Indicator Tool: LACE+

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Challenges: Data• All Variables aren’t in EHR:

– Case-mix group (CMG) score reduces c-statistic (0.753 vs. 0.743) (van Walraven, Wong, & Forster, 2012)

– Alternate Level of Care (ALC) Status

• Disease Conditions vague (mild, moderate, severe):

– Difficult to interpret and maintain (Quan et al., 2005)

– Literature guidance out of date

• Documentation inconsistency (Problem List vs. Patient History)

• Source of truth: Registration vs. Clinical documentation

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Challenges: Clinical Interventions• Resource constraints

– Unable to address all high risk patients (~4,500/month)

• Risk stratification:

– Too many high risk patients identified who did not readmit (84.6%)

– Too many high utilization patients with low or medium risk scores

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Formation of Innovation Group • Membership

– Two physician champions

– Nursing/Nursing Informatics

– Clinical Analytics/Biostatistician

– Care Transition Managers

• Focus

– Concept development

– Version review and approval, ensuring tool fits into provider workflows

– Development of interventions

– EHR feasibility, maintainability, replicability

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Strategic Goal

• Create a predictive scoring tool:– Tailored to THR’s specific readmission populations

– All attributes must be available in EHR prior to discharge

– Improve the trustworthiness of the risk designation

• Focus intervention efforts on High Risk and Medium-High Risk patients

– Remain budget and resource neutral while providing complex case management to 100%

of high risk cases

– Providing manageable workloads

• C-stat goal of 0.78 to 0.80+– Elevate from a fair tool to a good tool

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Clinical/Nursing Informatics Responsibilities• Analytics applications: SAS EG and SPSS Modeler, Statistics

• Created test environment for algorithm changes and statistical analysis

• Identification of source of truth and documentation reliability

• Data mining and validation

• Dataset preparation

• Determining and prioritizing indicators for evaluation

• Evaluation of indicators and readmission risk

• Variable weighting/scoring

• Build and testing in EHR

• Training and implementation

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Texas Health Readmission Indicator List (THRIL)

• Systematic analysis

• Incremental change

• Careful evaluation of impact

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From LACE+ to THRILv1• Addressed source of truth issues

• Reweighted disease conditions based on readmission rates

• Utilized patient history documentation in addition to Problem List

• Added new conditions (sepsis, antepartum complications, pneumonia)

• Re-stratified risk categories

• Adjusted age ranges, admission counts, point assignments

• Added raw counts of ED utilization and hospital admissions to target high utilizers

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Case Management InterventionsLow risk = ≤ 28

– DC Education begins on day of admission; meds reconciled; follow-up appointment made by the CNL

Medium risk = 29-58– DC Education begins on day of admission; find a PCP if necessary; CTM makes follow-up

appointment; Meds reconciled; community resources as indicated

Medium-High risk = 59-80 – DC education begins on day of admission; CTM arranges home health, rehab, skilled care

based on criteria and patient acuity. Refer to Transition Housecalls if possible.

High risk = ≥ 81– Complex case management; assessment for advance directives, end of life planning,

palliative care / hospice appropriateness

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Readmission Rate for ‘High Risk’ Patients

15.4% 15.4%16.6% 14.6% 14.7%

29.2% 28.4% 24.7% 26.0% 23.5% 26.0%

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16

Coun

t of

Patients

Cate

gori

ze

d

as H

igh R

isk

Readmitted

Not Readmitted

The height of each bar represents the total number of patients categorized as ‘High Risk’ for readmission.

The percentage displayed above each bar is the readmission rate for the ‘High Risk’ patient population. Higher percentages are better,

meaning we are identifying more readmitters in the High Risk bucket.

LACE+ THRIL v1

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Comparison of LACE+ to THRILv1

0.66

0.68

0.7

0.72

0.74

0.76

0.78

0.8

C-s

tat S

core

Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16

LACE+ 0.712 0.721 0.73 0.744 0.754 0.75 0.748 0.744

THRIL v1 0.784 0.771 0.77 0.777 0.761 0.76

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Where Are We Going?

LACE + Updates THRIL v1

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From THRILv1 to THRIL v2

– Medical History list count

– Surgical History list count

– Allergy list count

– Schedule I & II allergy count

– Braden Score <19 at discharge

– Existence of a Pressure Ulcer

– Count of pain score of 10 is reported

– Isolation status

– Marital status

– Payer

– Substance abuse

– Behavioral Health diagnosis

• Expand Palliative Care programs

• Widen focus to include Medium-High risk patients

• Incorporate new attributes:

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Lessons Learned– The making of a predictive tool is not a short-term project

– Requires dedicated resources and project management

– Allow for ample time to test and adjust scores and weights

– Avoid scope-creep

– Study the marketplace for attributes

– Be patient

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ReferencesQuan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J., . . . Ghali, W. (2005). Coding

algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43(11), 1130-1139.

van Walraven, C., Wong, J., & Forster, A. (2012). LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med, 6(3), e80-e90. Retrieved September 2016, from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659212/

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Questions

TannaNelson@texashealth.org

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Patient Generated Data Driving InnovationSession ID: NI2 February 19, 2017

Sally Okun, VP Advocacy, Policy & Patient Safety

PatientsLikeMe

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About PatientsLikeMe

Our mission is to improve the lives of patients through new knowledge derived from

shared real-world experiences and outcomes

• Founded in 2004 as a direct response to one

family’s experience with chronic disease

• Online, open, patient-facing community for patients

living with and managing illness

• Started in ALS, expanded to any condition in 2011

• Deep patient data and experience in many life-

changing conditions

• 30+ million structured data points

• 3+ million free-text posts

• 15+ PRO measures

• 460,000+ patients

• 2,500+ conditions

• 90+ peer-reviewed publications

• Patient-generated taxonomy

• Patient-informed principles

Patients Data Insights

Basic Information (age, sex, etc.)

Diseases, Conditions(early signs, diagnosis status, etc.)

Treatments & Side Effects(Rx, OTC, Supp., non-drug, etc.)

General & Specific Symptoms(onset, severity status, etc.)

Quality of Life & Behavior Status(all patients, some disease specific)

Outcome Measures of Disease(disease dependent)

Patient-generated narrative data in forum

discussions, journals and feeds

Emerging data source experiments(wearable/sensors, EHRs, claims, 'omics, specimens)

Engagement

Knowledge

Evidence

Standards

Data Integrity

Empowerment

Patient Data Conventions

Patient voice

translated into

computable

clinically relevant

data elements

Data codified using:

• ICD10

• SNOMED

• MedDRA

• ICF

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Patient-informed principles

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• Sufficient rigor for peer-review publication in leading journals

• Publish open access wherever possible

• Every patient has opportunity to take part in research if they want

• Use of IRB to ensure ethical conduct & oversight

• Describe patient reported and generated data objectively

• Provide “givebacks” to show patients the value of taking part in research

• Uphold the core values of PatientsLikeMe established in 2004:

o Patients first – always

o Honor patients’ trust – always be principled stewards of patients' data

o Transparency – always be clear about who we are working with

o Openness – always empower bidirectional sharing and communication

Research science principles

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Understanding unmet needs

Uncontrollable yawning in ALS • Some ALS patients reported yawning

dozens of times per day, sometimes

painfully dislocating their jaws

• We added “Excessive yawning” to

symptom list and gathered data from

254 ALS patients in just 4 weeks

• Found association between yawning

severity and patients whose first

symptoms were in their mouths and

throat vs limbs

• Impact: Identified possible drug

target, unmet need for symptom

relief, and contributed to medical

hypothesis generation

mild926 patients (36%)

none1058 patients (41%)

moderate503 patients (19%)

severe94 patients (4%)

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Quality of care in epilepsy

• Partnered with American Academy of

Neurology to develop a self-report

instrument to illuminate patient experience

with current state of care

• Found significant differences between

physician types; patients treated by non-

specialists receiving less quality care

• Identified care gaps around side effect

management, surgery referral, reproductive

issues in women

• Impact: Lead to changes in neurology

training and informed quality measures in

epilepsy for National Quality Forum

Illuminating care differences

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Sleep issues more prevalent in chronic illness

• Insights from patient-reported data to inform future clinical development

• Cross condition study of 51,000 with insomnia and survey of 5256 patients across 11 comorbid conditions

• Most patients not diagnosed with sleep disorder and consequently not being treated for sleep problems

• Impact: Insights shaped methods of education; identified strong link between chronic conditions and sleep problems; targeting for clinical trialsnyt

Insights about daily life

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Empowering action

Patient-Led Trial of Lithium to Slow ALS • A small Italian study suggested lithium carbonate significantly slowed ALS (N=16 treated)

• Over 160 PLM patients sought lithium from providers and tracked their outcomes

• We developed a matching algorithm using historical controls instead of a placebo group

• Refuted findings of original study within 6 months

• Impact: Hypothesis to result 3-5 years faster than multiple phase III RCT’s. Patients stopped using ineffective treatment

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...in our increasingly connected and networked world

data reported and generated by our real lived experiences

is essential if we are to achieve the promise of a

continuously learning health system

To learn, listen well to impressions voiced by patients first~sally okun~

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Questions

Sally Okun

sokun@patientslikeme.com

Twitter: @SallyOkun

LinkedIn: https://www.linkedin.com/in/sally-okun-3139a02

Please remember to complete your online session evaluation

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Dancing with Disruption February 19, 2017

Kimberly Ellis Krakowski, MSN, RN, CENP, CAHIMS

Associate Chief Nursing Information Officer

Inova Health System

Organization logo(s) may be placed on this slide

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Speaker Introduction

Kimberly Ellis Krakowski, MSN, RN, CENP, CAHIMS

Assoc. Chief Nursing Information Officer,

Sr. Director of Informatics and Clinical Applications

Inova Health System

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SBAR

Situation

• Inova Health System was approached by Booz Allen Hamilton to partner and

conduct research using wearable devices and proprietary algorithms.

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SBAR

Background

• While the first wearable device was created in 1961 by two mathematicians

to cheat at roulette, it was when the original FitBit electronic activity tracker

entered the market in 2009 that it became a popular household item rather a

newsworthy technology.

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SBAR

Assessment

• The organization’s EHR offers functionality to import a patient’s wearable

health device data into the patient portal and to become part of the patient’s

health record where it will be reviewed by a health provider.

• The organization made the decision to configure this functionality.

1. Congestive Heart Failure patient population for monitoring activity, HR

and weight.

2. Post operative orthopedic patients for monitoring activity levels.

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SBAR

Recommendation:• Research Option #1

– Provide Orthopedic patients wearable activity devices at discharge for

outpatient monitoring. The bedside nurse would be responsible for

provide education and documenting proficiency to the

patient/caregivers.

• Research option #2

– Provide nurses a wearable activity devices to measure activity and

sleep patterns and compare within rotating and non-rotating shifts.

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IRBObjectives• The primary objective was to remotely identify differential sleep

patterns between rotating/non-rotating nurses for sleep length and quality.

• The secondary objective was to identify optimum engagement methods with nurses by sharing their sleep patterns with encouragement to enhance sleep quality.

• An additional goal of this study are to expose nurses on the utility of wearable sensors for ultimate application towards future patients.

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https://www.youtube.com/watch?v=GA8z7f7a2Pk&sns=em

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Questions?

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Thank you!