Linking Low-Value Care Lists and Administrative Data to ... · Recommendations N=176 NICE’s Do...

Linking Low-Value Care Lists and

Administrative Data to Prioritize

Health Technologies for Reassessment

Lesley Soril, MSc (PhD Candidate)

HTA Unit, O’Brien Institute for Public Health

Department of Community Health Sciences, University of Calgary

CADTH Symposium – April 16, 2018

Disclosure

I have no actual or potential conflict of interest

in relation to this topic or presentation.

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The value of ‘low value’ care lists

“Do not do”

recommendations

> 150 low value

technologies

in Australian MBS

Unnecessary

tests and

treatments

To develop and implement a systematic

process, leveraging published low value

care lists, to identify and prioritize

candidates for reassessment

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STUDY OBJECTIVE

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Rapid-review

Environmental

scan

Clinical experts

Health system

decision-makers

Pilot test in BC

with BC MoH

Refining process

in Alberta

Approach

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Key process attributes

Data-driven

Routine & replicable

Stakeholder collaboration

Actionable

High return on investment

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• In-hospital admissions

• Physician claims

• Laboratory data

ADMINISTRATIVE HEALTH DATA

?

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Compile

Review + Coding

Frequencies + Costs

Rank + Prioritize

Review + Dissemination

5-step

process

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Compiled published low value

care recommendations

— Choosing Wisely Canada

(n=176)

— NICE “do not do”

recommendations (n=1000)

— Low value technologies in

Australian MBS (n=174)

#1 Compile

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#2 Review + Coding

Recommendations were

reviewed and excluded if:

— Drug technology

— Not publicly-funded

— Clinically “nuanced”: language or

qualifiers not identified in

administrative data*

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*Examples of “nuanced”

language:

— “asymptomatic”

— “uncomplicated symptoms”

— “unless red flags are present”

— “without alarm symptoms”

— “low-risk”

— “high-risk”

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Recommendations were

reviewed and excluded if:

— Drug technology

— Not publicly-funded

— Clinically “nuanced”: language or

qualifiers not identified in

administrative data*

Included recommendations

were coded using appropriate

coding system

#2 Review + Coding

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“Do not offer pulmonary artery catheterization to people with acute heart failure”

ICD-10 CCI: 2.IM.28.^^ (Pressure measurement, pulmonary artery)Includes: Catheterization, Swan GanzMonitoring, blood pressure, pulmonary arteryMonitoring, pulmonary wedgeRight cardiac catheterization

ICD-10-CA: I50.0 (Congestive heart failure)150.1 (Left ventricular failure)150.9 (Heart failure, unspecified)

ICD-9-CM: 428.0 (Congestive heart failure)428.1 (Left heart failure)428.9 (Unspecified)

Fee Codes: 49.95 (Right cardiac catheterization)49.95A (Right cardiac catheterization with fluoroscopy)

In-h

osp

ital

Cla

ims

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Query administrative data:

— Discharge abstract database

(DAD)

— Physician claims

— Laboratory data

Outcomes of interest:

— Total frequencies and costs

per year (over past 5 FYs)

#3 Frequency + Costs

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Results ranked by total claims

and hospital costs per FY

Prioritization filter: high

budgetary impact

— Composite measure of high

cost and volume

— Operationalized as total costs

> $1M in a FY

#4 Rank + Prioritize

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Prioritized technologies

reviewed by expert advisory

committee

— In-person meeting

— Ad-hoc with individual

clinical stakeholders

Documented and

disseminated to BC MoH and

health authority stakeholders

for consideration

#5 Review +

Disseminate

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Pilot

testing in

British

Columbia

Choosing Wisely

Recommendations

N=176

NICE’s Do Not Do

List

N=1000

Australian MBS

Low Value List

N=174

Technologies for

Review & Coding

N=1350

Technologies to

Query in Databases

N=74

Excluded Technologies

N=1276Drug Technology

Clinically Nuanced**

Not publicly financed

No ICD codes available

Duplicate

Technologies with Identified

Frequencies and Costs

N=32

Zero frequency N=42

High Budgetary ImpactFilter:

Claims and hospital expenditures

exceed $1M in a FY

Expert Stakeholder

Review and Feedback

Draft Prioritized List of Candidate

Technologies for Reassessment

N=9

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Conclusions

Systematic process to identify and prioritize low

value technologies at a population-level

Demonstrates feasibility and strength of using local

administrative data assets for reassessment

Adopted and operationalized by BC MoH

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Success dependent on strong

health system leadership

Necessity of broad health

system partnerships

Data as the process backbone

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Next steps: Alberta testing

Considerations & Limitations Solutions for Alberta testing

Recommendations excluded due to

clinically “nuanced” language

Collaboration with Alberta’s Strategic

Clinical Networks

Only examined non-drug technologies Collaboration with Alberta Health’s

Pharmaceutical Portfolio

Data infrastructure is context-

dependent

Responsive to Alberta data assets and

holdings

Research Team: (*co-lead)

Dr. Fiona Clement*

Dr. Craig Mitton*

Dr. Stirling Bryan*

Brayan Seixas

Acknowledgements

Partners:

lsoril@ucalgary.caContact: @lsoril

Funders: