Implementation and Dissemination Science (DI) by Thomas McGinn, MD by commonspirithealthresearch

Dissemination and Implementation (DI) Science: Closing the Evidence GAP

Closing the Gap Evidence and Clinical Practice

Evidence Clinical Care

Why Close the Gap?

• United States citizens receive only half of evidence based recommended care

• A third of all care received is thought to be unnecessary

Evidence - Practice gap = $380 billion of waste each year ….not to mention the harm

•

Closing the Gap Evidence and Clinical Practice

Evidence Clinical Care

Closing the Gap Dissemination and Implementation Science

Evidence Clinical Care DI

(DI)

Closing the Gap

Can EMR/Clinical Decision Support Reduce the Evidence Gap? Reduce Waste? Reduce Harm?

Evidence Clinical Care

Closing the Gap

Can EMR/Clinical Decision Support Reduce the Evidence Gap? Reduce Waste? Reduce Harm?

Evidence

Clinical Care

What is Dissemination and Implementation Science?

A growing research field that examines the process by which scientific evidence is adopted, implemented, and sustained in communities and clinical environments

“Real World Studies”

What is Dissemination and Implementation Science?

Three Case Examples: Moving From Effectiveness to D & I

1. Clinical Prediction Rule for Strep Pharyngitis

2. Wells Criteria for Pulmonary Evidence

3. Screening Tool for Child Abuse

Criteria for Selecting a CPR for Integration

1. Evidence Quality: Identify Level I and II

2. Evidence to Impact Healthcare Outcomes

3. Ease of Integrating at the point-of-care

4. Evidence consistent with providers’ perspectives

14 Derived Only Level IVNarrow Validation Level III + Validated broadly Level II ++ Impact analysis Level I +++

JAMA July 2000, McGinn, Guyatt, Wyer, Stiel, Naylor Clinical app

Hierarchy of Evidence

Case Study 1

● Clinical prediction rule for predicting Streptococcus pharyngitis and need for antibiotics

●Recent cough (-1)

●Strep exposure (+1)

●Tonsillar exudates (+1)

●Enlarged, tender cervical nodes (+1)

●Fever >100.8F (+1)

Rationale

•Up to 50% of antibiotic prescriptions for acute respiratory infections (ARIs) are inappropriate

•This leads to antibiotic resistance, patient morbidity, and unnecessary expense

•Healthcare information technology is a potent tool for improving healthcare quality

Pt Evaluated Clinical Prediction Rule Calculator Low risk Intermediate Risk High Risk Intermediate Ordering Set Low Risk Bundled Ordering High Risk Bundled Ordering

CPR Use Case Point of Care

Clinical Prediction Rules into an Electronic Health Record: iCPR

Integrating

Study Randomized Control Trial of CPRs

Study Design

Results: Process Measures

Study Sample: 166 primary care providers

87 Intervention, 79 Control

•

• 66%

Intervention Arm Encounters Strep N (%) PNA N (%) Tool Activated 374 212 Provider Accepts Calculator Provider Signs Bundled Orders 189 (51) 57 (27)

residents

Results: Primary Outcome

Intervention (n=586) Control (n=410) N (%) N (%) OR (95% CI) P value Antibiotic orders from all encounters 171 (29) 156 (38) 0.64 (0.46-0.90) .01 Strep encounters 56 (15) 46 (20) 0.72 (0.46-1.13) .15 Pneumonia encounters 115 (54) 110 (62) 0.58 (0.35-0.99) .04 X-ray orders from pneumonia encounters 45 (21) 37 (21) 0.86 (0.46-1.59) .63 Rapid strep orders from strep encounters 109 (19) 97(42) 0.64 (0.43-0.95) .03

Five year NIH Funded RCT with one year of design and usability

New York (NYU-Mann) (NWH-McGinn), Wisconsin (UWFeldstein), Utah (UU-Berger), Boston (BU-Mishuris)

33 Primary Care sites

Family Medicine

Internal Medicine

Dissemination of iCPR – iCPR 2

•

• ANP

iCPR2 Study

● Northwell, NYU, Boston University, University of Utah and University of Wisconsin

● Utah and Wisconsin were sites of implementation

● 33 primary care practices involving 177 PCPs

● However, adoption 6%, no impact on antibiotic ordering

● Alert fatigue responsible for low adoption

CPR 3: Nurse Triage and Workflow

Case study 2: CPR for Pulmonary Embolus The Wells’ Criteria

26 Score range Mean probability of PE Interpretation of risk <2 points 3.6% Low 2 to 6 points 20.5% Moderate >6 points 66.7% High Criteria Points Clinical signs and symptoms of DVT +3 PE is #1 diagnosis, or equally likely +3 Heart rate>100 +1.5 Immobilization at least 3 days, or surgery in the previous 4 weeks +1.5 Previous, objectively diagnosed PE or DVT +1.5 Hemoptysis +1 Malignancy with treatment within 6 months or palliative +1

Clinical Prediction Rules for Pulmonary Embolism

• Well-validated Level 1

• Not used consistently

• Overuse of costly diagnostic tests: CT-angiography with high false positive rate

• CTA overused nationally: 20-40% • Northwell CTA/PE Rate:

⮚ 250-300 CTA/1 PE----- 150-200 CTA/1 PE

Clinical Workflow at LIJ Emergency Department

Patient Enters ED

YES

Diagnositc testing

Pt is roomed

MD Assessment

Attendee, Residents in exam room

Diagnostic testing required? Treatment

Pt is discharged and leaves ED or admitted to hospital for observation as per MD’s orders

Triage station PA/RN Triage Priority level 15 determined

Simplified VTE Clinical Decision Support ER Workflow

29 Calculator completed by RN or MD Moderate risk

Nurse ER Physician

Triage

PE CPR: Northwell Health: 30% Reduction in CT scans

Case study 3: Disseminating Child Abuse Clinical Decision Support: Improve Detection, Evaluation and Reporting

The Why

• CPS 700,000 children were victims of maltreatment last year

• 4 million referrals of abuse or neglect made to Child Protective Services (CPS) in 2019– at least 1,670 children died of child maltreatment

• Medical professionals do NOT consistently screen for abuse even in high-risk situations

• Many children who are injured or die due to physical abuse have been previously evaluated by a physician for injuries BUT the diagnosis of abuse as the cause of the injury was not recognized

Collaborators

Population reach

NW UW # of pts eligible to be screened for child abuse or trigger one of the other CA-CDSS components 47,943 patient visits (4 sites) 11,396 patient visits (2 sites) ED providers who received training 266 providers 68 providers The number of ED nurses at the participating sites 469 152

A breakdown of reach by site

SITE Number of patients reached Number of providers Cohen Children’s Medical Center 31,099 encounters 71 providers Long Island Jewish Forest Hills 3223 encounters 57 providers North Shore University Hospital 2321 encounters 67 providers Staten Island University Hospital (north and south) 11,300 encounters 71 providers NW (ALL SITES) 47,943 encounters 266 providers UW Health BerbeeWalsh ED supporting the American Family Children’s Hospital 9950 encounters * UW Health at The American Center 1446 encounters * UW (ALL SITES) 11,396 encounters *68 providers (many providers work at both sites)

The primary objective: implement CA-CDS in 2 EHRs in 2 hospital systems

Hypothesis #1: Identification of cases of possible child maltreatment will increase when CA-CDS is embedded in the EHR.

Child Abuse Screening Tool

ED Child Abuse, Pediatric Order Set

Review of Triggers for Alert Pop-up

Triage nurse enters Chief Complai nt

Primary nurse answers yes to question( s) on Universal Screen (<13 yo)

Status board alert

Patient gets evaluate d by the physicia n

Physicia n orders (i.e. skeletal survey)

What impact did we have?

• Important impact on clinical care- routine child abuse screening in all children <13 years old at NW and <10 years old at UW

• The screening rates for the CAS were impressively high at both sites and have been sustained since the end of the live period as the sites move this into clinical care.

Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Percent CAS Complete April 2019 - April 2020 UW %CAS Complete NW %CAS Complete

Three validated clinical prediction tools Challenges to Implementation

Themes:

• Cultural Challenges of adopting prediction rules

• Difficult to integration into EMR

• Complex clinical environment with varying workflows hard to influence

• Difficult integration with outside agency

• Complex changes in diagnostic/therapeutic regimens

Three validated clinical prediction tools in Research Trials

Challenges to Implementation

Themes:

• Cultural Challenges of adopting prediction rules

• Difficult to integration into EMR

• Complex clinical environment with varying workflows hard to influence

• Difficult integration with outside agency

• Complex changes in diagnostic evaluation

Bottom Line: Change is Hard

But Change is Urgently Needed

Monitoring EHR for order entry trigger

CDS Universal Platform

Clinical decision support tools

Risk assessment evaluated from calculators

Ability to push and pull data

24/7 order and usage monitoring

documentation

Patient

What is Dissemination and Implementation Science?

A growing research field that examines the process by which scientific evidence is adopted, implemented, and sustained in communities and clinical environment”