What is Implementation Science and why should you care?

JoAnn E. KirchnerProfessor, Department of Psychiatry, UAMS

VA Team-Based Behavioral Health

Incorporating Implementation Science to Support Core Clinical Competencies: An Overview and Clinical Example, (in review) JoAnn Kirchner, Eva Woodward, Jeffrey Smith, Geoff Curran, Amy Kilbourne, and Mark Bauer.

Presentation Overview· Overview of Implementation Science

Frame Implementation Science within the translation continuum Definitions Factors that Impact Implementation

· Clinical Case

· Example of an implementation science intervention

· Revisit our Clinical Case

· Discussion

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Pre-intervention

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Efficacy Studies

Pre-intervention

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Effectiveness Studies

Efficacy Studies

Pre-intervention

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Dissemination and Implementation

Studies

Effectiveness Studies

Efficacy Studies

Pre-intervention

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Dissemination and Implementation

Studies

Effectiveness Studies

Efficacy Studies

Pre-intervention

Health-Related Research Implementation

Brownson, Colditz, Proctor, 2012

Dissemination and Implementation

Studies

Effectiveness Studies

Efficacy Studies

Pre-intervention

T1

T2

T3

T4

Health-Related Research Implementation

Dissemination and Implementation Studies

Sustainment

Implementation

Preparation

Exploration

Definitions

Dissemination

Definitions

Dissemination Quality Improvement

Definitions

Dissemination Quality Improvement

Implementation

Definitions

Implementation SCIENCE: “The scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine care…..”

Eccles and Mittman, Implementation Science, 2006

Implementation Science Aims

Implementation Science aims to: Develop effective strategies for improving health-

related processes and outcomes

Produce generalizable knowledge regarding implementation processes, barriers, facilitators, and strategies

Develop, test, and refine implementation theories and hypotheses, interventions, and measures

Factors that Impact Implementation

integrated - Promoting Action on Research Implementation in Health Services Framework

i-PARIHS Framework

SuccessfulImplementation

Context

SuccessfulImplementation

• Inner context: local and organizational • leadership support• culture• organizational priorities

• Outer context • policy drivers and priorities• incentives and mandates• inter-organizational networks

Innovation

• Relative advantage • Usability• Trialability• Evidence

• research-based evidence • clinical experience• patient preferences

and experiences

SuccessfulImplementation

Recipient

• Motivation• Values and beliefs• Goals• Skills and knowledge• Time• Resources and support• Local opinion leaders• Power and authority

SuccessfulImplementation

Context

Innovation

Recipient

SuccessfulImplementation

Context

Innovation

Recipient

SuccessfulImplementation

Facilitation · Arose from the education and nursing discipline

· Acknowledges that while research evidence is important, clinical experience and professional knowledge directly impact adoption

· Multifaceted process

· Bundles an integrated set of implementation strategies Which strategy is applied varies based on the needs of the

implementation process

· Dynamic in nature that involves interactive problem solving

Context

Innovation

Recipient

SuccessfulImplementation

Facilitation

i-PARIHS Framework

Clinical Case: Mr. A

Clinical Case: Mr. A

Dr. C is a psychiatrist practicing within a large integrated healthcare system that provides primary and specialty care. He prides himself on being current with recommended standards of care and evidence-based treatments. Mr. A is a 33 y/o single male with a ten-year diagnosis of schizophrenia who presents as a new patient without any prior medical records. He reports that he has done well in the past when treated with olanzapine but has not been on medication for six months. He exhibits mild psychotic symptoms including occasional non-command auditory hallucinations, confused thinking, and social isolation.

After a thorough evaluation, Dr. C confirms the schizophrenia diagnosis and no history of diabetes, and restarts Mr. A on olanzapine 15 mg daily, requesting that he see the receptionist to be weighed before leaving and then to go to the lab for a baseline hemoglobin A1C and lipid profile. His return appointment is made for four weeks.

Clinical Case: Mr. A

When Mr. A returns, he displays no confusion, reports almost complete resolution of auditory hallucinations, but continues to report social isolation. He states that he left immediately after his appointment and did not get weighed or go to the laboratory. Dr. C continues olanzapine at the current dose and provides directions to the receptionist for a weigh-in and asks the receptionist to direct Mr. A to the laboratory.

A Study of Strategies to Improve Schizophrenia Treatment

(ASSIST)

Dr. C’s clinic

Contextual Factors The chief of psychiatry conveyed the importance of metabolic monitoring in several staff meetings after

a new performance standard was introduced at the hospital

Innovation Because antipsychotic side effect monitoring was an evidence-based practice, clinic staff believed it

was clinically valuable

However, staff were not accustomed to prioritizing antipsychotic side effect monitoring, and competing time demands led to prioritizing other important measures such as suicide risk assessments

Additionally, a computerized clinical reminder for metabolic side effect monitoring was not perceived as helpful by clinicians

Recipient Providers were quite open to the evidence that antipsychotic monitoring was clinically valuable

However, two clinicians who were well-respected among the staff (“opinion leaders”) regularly complained about any new performance measures, and believed that because standards changed so often, none were valid or important

ASSIST

· Multifaceted implementation strategy to improve metabolic side effect monitoring for patients with schizophrenia who were prescribed antipsychotics in Dr. C’s out patient clinic

· Utilized a local QI team comprised of opinion leaders involved in medication management of patients with schizophrenia and an external facilitator

· Facilitator identified local barriers to recommended metabolic side effect monitoring

ASSIST

· Initial efforts only produced modest (10-15%) improvements and by the third month, rates had returned to almost baseline

· The facilitator re-engaged with the local QI team for ideas on strategies that could produce sustainable improvements

· Dr. C suggested the monthly performance reports, while helpful, were not timely in identifying patients who had not been monitored in compliance with performance standards

· The facilitator worked with IT staff to develop a computerized report emailed to Dr. C on a weekly basis identifying patients due for metabolic monitoring

ASSIST

· Dr. C used the information to contact clinicians to encourage them to complete metabolic monitoring

· At the end of the 6-month implementation period, the proportion of patients whose weight was monitored as recommended increased from 70% to 93%, with dramatic increases in glucose and lipid monitoring rates also (53% to 80% and 29% to 67%, respectively)

· For the first time ever, the clinic was compliant with the performance standards for metabolic side effect monitoring, and the clinic remained in compliance at one-year follow up

Clinical Case: Mrs. B

Clinical Case: Mrs. B

Prior to her return appointment, Dr. C receives a computerized report noting that Mrs. B had not received metabolic monitoring following a new prescription of an antipsychotic. Dr. C contacts Mrs. B and directs her to the lab. Dr. C receives a lab alert noting that Mrs. B’s fasting glucose is 180. Dr. C tapers the olanzapine and initiates aripiprazole Mrs. B responded well without exacerbation of her psychotic symptoms and no abnormal glucose, lipid or weight changes.

Resources

· Introduction to Implementation Science, COPH, Dr. Geoff Curran

· Implementation Science , the JournalDissemination and Implementation Research

in Health: Translating Science to Practice. Edited by Ross C. Brownson, Graham A. Colditz, Enola K. Proctor Oxford University Press, NY:NY 2012

Discussion