Population Health, Informatics and Quality

Improvement

PQCNC Learning Session May 8, 2014

Arthur Ollendorff, MD MAHEC OB/GYN Specialists Asheville, North Carolina Clinical Professor of OB/GYN University of North Carolina SOM Arthur.Ollendorff@mahec.net

Disclosures

¢  PQCNC Co-director of Maternal Projects l  0.1 FTE

¢  Physician informaticist for Mission Health l  0.1 FTE

Another Disclosure

Objectives

¢ To review the basics of population health and its potential impact on new healthcare payment models

¢ To discuss the benefits and limitations of medical information systems

¢ To understand the role of the informatics team in the development and analysis of quality improvement initiatives

We all wear many hats

¢  My hats include l  OB/GYN l  Medical educator l  “Quality Guy” l  Informaticist

A Tale of Two Pyramids

Informatics Pyramid Evidence Pyramid

Population Health

¢  “The health outcomes of a group of individuals, including the distribution of such outcomes within the group”

¢ The field of population health includes health outcomes, patterns of health determinants, and policies and interventions that link these two

Kindig D and G Stoddart. American Journal of Public Health March 2003: Vol. 93, No. 3, pp. 380-383.

An unofficial, simplistic and pragmatic definition

¢ Population health uses data to analyze the health attributes of a population and then uses evidence-based decision making to improve the health of that population

What population to look at?

¢ An individual physician’s patient panel ¢ An office/clinic’s patients ¢ Neighborhood ¢ Town/City ¢ State ¢ Region ¢ National

Accountable Care Organization

¢ Groups of doctors, hospitals, and other health care providers, who come together to give coordinated high quality care to their patients l The goal is to get the right care at the

right time, while avoiding unnecessary duplication of services and preventing medical errors

l Share in the savings it achieves

Adapted from http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO

Institute for Health Improvement Triple Aim

¢  Improving the patient experience of care

¢  Improving the health of populations

¢  Reducing the per capita cost of health care

Essential Elements of an ACO ¢  Culture of Teamwork ¢  Primary Care ¢  Administrative

Capabilities ¢  Financial Incentives ¢  HIT and Data ¢  Best Practices ¢  Patient Engagement ¢  Patient Population

Scale

Edmiston G and D Wofford, Physician Alignment: The Right Strategy; the Right Mindset, Healthcare Financial Management Association (December 1, 2010).

Essential Elements of an ACO ¢  Culture of Teamwork ¢  Primary Care ¢  Administrative

Capabilities ¢  Financial Incentives ¢  HIT and Data ¢  Best Practices ¢  Patient Engagement ¢  Patient Population

Scale

Edmiston G and D Wofford, Physician Alignment: The Right Strategy; the Right Mindset, Healthcare Financial Management Association (December 1, 2010).

Health Information Technology and Data

¢  How reliable is the data we currently use?

¢  How many different data systems are involved?

¢  How easy is it to get the reports we need?

The Basis of Best Practice

Health and Informatics

¢  Informatics l  the science of information

¢ Health Informatics l putting information and knowledge to

use in promoting health and improving health care

Informatics Pyramid

Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis 15: 3-9.

DIKW

¢  Data l  the product of observations

¢  Information l  contained in answers to questions

¢  Knowledge l  “Makes possible the transformation of information into

instructions. It makes control of a system possible.”

¢  Wisdom l  ability to see the long-term consequences of any act

and evaluate them relative to the ideal of total control l  “omnicompetence”

Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis 15: 3-9.

“Big Data”

¢  A collection of data that represents a source for ongoing discovery and analysis l  Unstructured data

•  information that is not organized or easily interpreted by traditional databases or data models, and typically, is text-heavy

l  Multi-structured data •  data formats that can be derived from interactions

between people and machines

“What Is Big Data”. Forbes.com. 8/15/2013

The Evidence Pyramid

Two research study designs

RCT’s ¢  Study design controls the

intervention ¢  Requires a lot of upfront

planning and design ¢  Results are

straightforward to analyze ¢  “Pure”

Cohort Studies ¢  The intervention is less

under control ¢  Does not require too

much planning and design

¢  Results require more statistical expertise to analyze

¢  “Real world”

QI Initiatives = Cohort Study

¢  More work occurs in data acquisition and analysis

¢ Data is analyzed from actual patient care encounters

¢ The need of an expert to assist l Cohort study – Statistician l QI project – Informatics team

Is There a Quality Link to This Story?

The New Realities*

¢ There will be increasing demand for health outcomes data at our institutions

¢ Some of this data will be high stakes data that will determine payment and resource allocation

¢ QI initiatives will, at times, compete for informatics resources

*with apologies to Peter Drucker

How Is Your QI Data Gathered?

Automated Hand Counted

Technology

Limitations of Using EHR for Quality Improvement ¢ EHRs are designed for clinical care

l  there is a great deal of unstructured, free text data

l nurses and providers, many who are untrained in the quality arena, are entering the data

¢ Medicine is complex and patients do not always fit into a category

What is the “Cure”?

1.  Throw money at the problem 2.  Hope that a renewed focus on value

will make nurses/providers better at entering data

3.  Develop clinically relevant systems that improve patient care and yield usable data for quality review

Initial Steps

¢ Know the capabilities and limitations of your EHR

¢ Recognize that design (data input and workflow) are critical

¢ Control what is in the foreground (what the providers see) and what is in the background (data that can be used but not visible)

The Ideal Informatics Team

¢  Knows the capacities, limitations and potential of the data systems in use l  EHR corporate rep

¢  Can extract data from the system l  IT person who works in a service line

¢  Has clinical experience to know what providers and nurses use in daily practice l  Nurse and physician “informatics types”

Angel

Transylvania

Mission Hospital

Blue Ridge

McDowell Hospital

Mission Health Hospitals Providing OB Care

Mission Health Women’s Service Line IT Approach ¢  Leverage Cerner Powerchart Maternity

to provide l  JCAHO Core Perinatal Measures l  PQCNC Data l  Data we suspect might be important

•  Induction of labor

¢  Drive quality and patient safety with usable templates for nurses and providers that are similar across all hospitals

Mission Health’s Timeline ¢  June 2012

l  Decision made to upgrade to Powerchart Maternity

¢  September 2013 l  Worked with providers to develop a clinically useful

note l  Worked with nurses to develop useful charting tools

¢  October 2013-March 2014 l  Developed and vetted six OB Powernotes and

numerous nursing iViews

¢  March 22, 2014 l  Go Live

Mission Health Admit H&P

Mission Health Delivery Note

Summary

¢ There is not a roadmap to maximize informatics at your institution

¢ Seek out opportunities to align your goals with that of your institution l ACO (value-based) outcomes l HEN/PQCNC initiatives

¢ Develop a team to design a system to improve data acquisition

Conservative Management of Preeclampsia

(C-MOP)

The Rest of This Afternoon’s Gameplan ¢  12:30 Team presentations of PDSA,

challenges, successes ¢  2:50 Break ¢  3:00 Teams work together to

l come up with next PDSA l decide 'what are you gong to work on

as soon as you get back?' l  'report back’

¢  4:30 Closing remarks

Aim

To provide the education and support necessary to develop standards of care in NC hospitals for the patient with preeclampsia

Outcomes

¢ Primary l To eliminate deliveries at less than 37

weeks gestational age for women with preeclampsia without severe features

¢ Secondary l Administration of antenatal

corticosteroids to women who deliver < 34 weeks with preeclampsia

PDSA Cycle Presentations

¢ Each team will have 5 minutes to discuss their progress, successes and challenges of their PDSA cycle

¢ There will be 3 minutes for feedback and discussion

¢ Teams will be chosen at random

C-MOP Participating Sites

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Cape Fear Valley Caromont Cleveland Regional CMC-Main CMC-Northeast CMC-Pineville Columbus Duke Forsyth Granville

McDowell Mission New Hanover Novant-Huntersville Presbyterian Rex Transylvania UNC Vidant Wake Med Womack

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February 4, 2014

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Randomly Generated Presentation Order

1.  McDowell 2.  Mission 3.  New Hanover 4.  CMC-

Northeast 5.  CMC-Pineville 6.  Novant-

Huntersville 7.  Cape Fear

Valley 8.  Caromont 9.  Presbyterian

10.  CMC-Main 11.  Rex 12.  Wake Med 13.  Vidant 14.  Duke 15.  Columbus 16.  Cleveland

Regional 17.  Forsyth 18.  Transylvania 19.  Womack

20.  Granville 21.  UNC

The Next Step

¢  3:00 Teams work together to l come up with next PDSA l decide 'what are you gong to work on

as soon as you get back?' ¢  3:45 ‘Report Back’

l Share ideas l Use the larger group as a sounding

board