Data Acquisition and informed Retirement

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USING (BIG) DATA TO

EFFECTIVELY EMPOWER TOMORROW'S

NURSE LEADERS

Dr. Robert J. McGrath

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0 5 10 15 20 25 30 35 2009 2013 2015 2020 0.79 4.1 7.9 35

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WHAT IS A ZETTABYTE?

1,000,000,000,000 Gigabytes 1,000,000,000 Terabytes 1,000,000 Petabytes 1,000 Exabytes 1 Zetabyte 1 Terabyte holds about as much as 210 DVDs

10%

Structured

These are the data that exist in databases

10%

Structured

These are the data that exist in databases

90%

Unstructured

Sensors, pictures, video, audio. These are the elements people and machines generate regularly, and are most of the story to be told.

90%

Unstructured

Sensors, pictures, video, audio. These are the elements people and machines generate regularly, and are most of the story to be told.

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EMR Org. Info Research _Info Quality of Care Treatment Decisions Demogr Health Insurance Knowledge generation Decisions

Prediction Visualization Reporting

ETL Data Mining Data Integration

Data Collection and Storage

Sander Klaus, KPMG http://www.slideshare.net/sanderklous/big-data-in-healthcare

Internal Data External Data

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0%

100%

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• In 2012, OECD Countries spent $59 Billion in biomedical

research

• Bayer could replicate only 25% of 67 studies

• Amgen only 11% of 53 studies

• Two studies: 1500 BMJ reviewers missed 92% of errors

• Even in RCT studies, reviewers failed to detect important

deficiencies of 93 control studies

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• John Ioannidis at Stanford University argues that most published findings are

false

.

• In February of 2014, Regina Nuzzo argues in Nature that P-values are highly

skewed.

• The more implausible the hypothesis — telepathy, aliens, homeopathy — the

greater the chance that an exciting finding is a false alarm, no matter what

the

P

value is.

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THE LOOMING DEMOGRAPHIC CONUNDRUM

1950 7.2 1980 5.1 2011 4.1 2050¹ 2.1 Living Longer

US Life expectancy for Males @ 65

1940: 12 years 2007: 18 years

Aging Beyond Our Ability to Support

623K New Medicare beneficiaries each year 1995-2010

1.6M New Medicare beneficiaries each year 2010-2030

2X

In 2030, Medicare will have twice as many beneficiaries as in 2010

Number of People 20-64 for Every Person >65

http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsProjected.html

CONFRONTING A CHANGING PARADIGM

The Evolution of Incentives for Providers

Fee for Service DRG / Quality Cost

Incentives Accountable Care

Patient Volume Length of Stay Ancillary Testing

Health Care Environmental Paradigm

• Volume driven primary & specialty care

• Emergence of quality & safety processes & metrics

• Increased transparency on pricing & outcomes

The “Triple Aim” (Value)

• Improve the experience of care

• Improve the health of populations

• Reduce the per capita costs of health care

• Two-way risk sharing

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GLOBAL PAYMENT IMPLICATIONS

EXAMPLES of “Re-Thinking” Care Delivery Systems Under Global Payment

Models

“Rapid Access Care”

ER use change

Diagnostic Testing

In system vs. out of system perspectives

Chronic Illness – Behavioral Health Impact

Implications for primary care delivery systems

Improving Population Health is Challenging

Better the Experience of Care Lower Per Capita Health Costs Improve Population Health

Better

Value

Transforming Health Care Delivery

System

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CARE SYSTEM TRANSFORMATION FOCUS AREAS

Enhanced Primary

Care

Care Management

Patient Engagement

and Activation

Post Acute Care

Partnerships

Aligned Payer

Relationships

Integrated

Information

Technology

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Core Integration Strategy:

Some Outcome Inefficiencies Media Enhanced Health Outcomes Providers Non-profits Schools Civic Groups

Government Businesses Public Health Faith-based Food environment

Fragmented system

Integrated system

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EMR Org. Info Research _Info Quality of Care Treatment Decisions Demogr Health Insurance Knowledge generation Decisions

Prediction Visualization Reporting

ETL Data Mining Data Integration

Data Collection and Storage

Sander Klaus, KPMG http://www.slideshare.net/sanderklous/big-data-in-healthcare

Internal Data External Data

CURRENT HEALTH CARE INFORMATION

TECHNOLOGY LANDSCAPE

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• GOOD AT:

• Large-scale institutional EHRs can facilitate communication within the

institution

• Hospital to emergency department to in-house medical clinic.

• BAD AT:

• Need for custom screens and solutions

• Clunky tools lead to department or office specific work arounds that don’t

easily translate

• And non HIPAA compliant alternatives like emails.

• Or…Using fax and phone

AMERICAN ACADEMY OF NURSING ON POLICY:

A CALL TO ACTION: ENGAGE IN BIG DATA SCIENCE

“No more evident is this than in nursing where after decades of implementing

EHRs nurses still cannot consistently use electronically collected data to

conduct research or report quality and patient safety outcomes.”

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http://authenticmedicine.com/growing- hospital-computer-information-system-stopped-stephen-mussey-md/

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HIPAA

• Covered: Data for clinical care is covered

• Not Covered:

• Data collected by a pharmaceutical manufacturer in a clinical trial

• Searches that people do online for health information

• Social media or mobile health apps to collected and store and use data.

• Stage 1

• 91% of eligible hospital and 68%of eligible providers have been paid in the EHR

Incentive Program

• Stage 2 Progress is beginning

• Requires enhanced capabilities for interoperability, patient engagement and

quality measures

• 893 certified products as of May 2014

• 8 hospitals and 252 providers have attested as of May 2014

• Stage 3 measures are being finalized

• ONC held first (annual) Nursing Summit

• 239 attendees; 80% from frontline nursing roles

MEANINGFUL USE

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• A large majority (82%) indicated that bi-directional sharing of

clinical and/or patient data with local healthcare organizations

is important or very important to their organization.

• Analytics for Quality improvement most common (90%)

• Inpatient care utilization and outcomes analysis (80%)

• Adverse event reporting (75%)

eHealth Initiative, Key Findings from eHealth Initiative Survey on Data and Analytics. August, 2013. http://www.ehidc.org/resource-center/publications/view_document/26

READINESS:

2013 CIO SURVEY

BUT…..

• Only 18 percent of respondents indicated that they have sufficient

trained staff to collect, process, and analyze data.

• Twenty-six percent report that although they have tried hiring more

staff for analytics, they have not found sufficiently trained candidates.

• Thirty-four percent of respondents note that senior leadership had not

prioritized analytics as a key area for staffing needs.

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DATA COLLABORATIVES

Academia Patients / Consumers IT Industry Clinical Delivery Public Health Payers / Employers Communities HIE’s

[case studies here]

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GE HEALTHCARE AND SAS ANALYTICS

• How to capture “near misses” in health care

• Airlines do it often (50:1 ratio)

• Health care its estimated at about 300:1

• There's been a tenfold increase in the number of events being captured by the GE Medical Event Reporting System (MERS).

OHIO MEDICAID: CARESOURCE CLAIMS ANALTYICS

Using claims, Ohio found:

• A patient with 27 unique doctor visits in one month and 30 different prescriptions written (seven of them just for asthma).

• Patients who were taking medicine that was contraindicated, prescribed by different doctors.

• Patients who visited the ER so many times for headaches that they were exposed to dangerous amounts of radiation because of multiple CAT scans.

By providing the data to nurse care managers they were able to improve quality:

• An office can generate a list of patients overdue for a blood sugar test and call the patients for reminder appointments.

• Prescriptions are immediately visible to doctors when a patient visits, allowing them to better manage a member’s health.

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NH AND APCD DATA

UNH Institute for Health Policy helped the Accountable Care Project across the state with the goal of: Creating and sustaining a payment reform/clinical/quality improvement learning network.

- APCD (all payer claims data)

METHODS – PROVIDER IDENTIFICATION, NPI REVIEW

Category % of claims in category “Fix”

1. Consistent NPI (No concerns) 46.09% None Needed

2. Consistent NPI when populated, but sometimes missing

9.62% Most prevalent NPI was assigned to the Service Provider ID

3. Always missing NPI 3.90% No fix attempted

4. Multiple, inconsistent NPI (could include some missing)

40.39%;

5.15% Changed NPI

Most prevalent NPI was assigned to the Service Provider ID

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NH ACO / APCD

• Other Issues:

• Defining Primary Care?

• Patient attribution

• Geographic representation (so as not to attribute costs by MSA)

• Report design (big challenge…)

• Site- and region-level reporting (2 sets)

• 11 clinical measures

• Reporting by 19 geographic regions

• Reporting by 21 site designations

• Reporting for 3 types of data (commercial, Medicaid, Medicare)

• 2 years of data

REPORT DESIGN: TOO MUCH INFO!

• All in PDF output

• More than 2,000 pages of

reports across full report suite

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SOLUTION

• SAS Visual Analytics

• Provides online secure portal

• Ability to drag and drop variables for a variety of cuts

• On the fly graphics and visualizations

PREDICTIVE ANALYTICS

UC Irvine Health:

• Had millions of data points across 1.2 million patients over 22 years in Excel files and

paper

• Needed to migrate to a singular data warehouse into a single platform (Hortonworks)

which fed medical center and the research center.

• The Key…HADOOP.

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PREDICTIVE ANALYTICS

UC Irvine Health:

• One outcome is clinical nursing. Patients wearing vital sign sensors transmit every

minute

• 4,320 per patient per day

• Using predictive algorithms, nurses get signals for near term health risk

outcomes.

• Those vitals can then be combined for other data on that patient or on historical

patiets with similar risk factors etc…

• Let the data uncover what was once hidden.

THE FUTURE:

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Start-Up Funding by digital health companies in 2014

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In here: What is a data scientist? Will there be a nurse or quality analytics role?

WHAT SHOULD NURSE LEADERS BE READY FOR?

BIG DATA CONFERENCE: EXPERT NURSING PANEL

2013

Recommendations

• 1. Implement strategies that advance the adoption of standardized terminologies for clinical documentation by nurses in electronic health records.

• 2. Conduct two policy updates via conference call/webinar for the expert panel membership ( June 2014 and September 1, 2014) following the Big Data Conference

• 3. Conduct an open “Policy Dialogue” entitled “Putting the Health Back in Electronic Health Records” at the 2014 Annual AAN Conference in October 2014

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A CALL TO ACTION: ENGAGE IN BIG DATA SCIENCE

• Develop a strategy/campaign for educating front line nurses, students, and faculty on informatics

competencies and the value of standardized nursing data.

• Advocate for the adoption of Systematized Nomenclature of Medicine - Clinical Terminology and Logical Observation Identifiers Names and Codes as national standards for clinical data, and link them with nursing terminologies through mappings.

• Convene a consensus conference with leaders of the major nursing organizations and

interprofessional stakeholders to educate them, hear their views, and ultimately speak in one voice.

• •Refresh and activate the American Nurses Association's Nursing Information & Data Set Evaluation Center criteria to advance systems that represent and value nursing data.

• •Continue bold participation in standards and EHR standards development to ensure a nursing voice

Members of the American Academy of Nursing's Expert Panel on Nursing Informatics and Technology attended the conference and participated in developing the following key components of the action plan: Nursing Outlook Volume 62, Issue 1, January–February 2014, Pages 64–65

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• Many industries moving to expanded roles for the advanced data consumer

• Chief Data Officer

• Chief Analytics Officer

• Director of Nursing Informatics / Analytics

• BUT…Data Science in Different than Informatics

• Beyond storage, retrieval and reporting it includes:

• Advanced analysis, predictive and prescriptive analysis

• The ability to distill and convey information both verbally, orally and visually