Evidence map›Paper›PMID 30288446›Full record

ArticleMDM policy & practice

Health Technology Optimization Analysis: Conceptual Approach and Illustrative Application.

Charles Yan, Yufei Zheng, Michael D Hill, Balraj Mann, Thomas Jeerakathil, Noreen Kamal, Shy Amlani, Anderson W Chuck

Open access · goldAbstract read
In one paragraph

Article in MDM policy & practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Forecasting of Future Medical Care Expenditure in Japan Using a System Dynamics Model.Inquiry : a journal of medical care organization, provision and financing
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Charles YanInstitute of Health Economics, Edmonton, Alberta, Canada.
Yufei ZhengInstitute of Health Economics, Edmonton, Alberta, Canada.
Michael D HillUniversity of Calgary, Calgary, Alberta, Canada.
Balraj MannCardiovascular Health & Stroke SCN, Alberta Health Services, Alberta, Canada.
Thomas JeerakathilUniversity of Alberta, Edmonton, Alberta, Canada.
Noreen KamalUniversity of Calgary, Calgary, Alberta, Canada.
Shy AmlaniAlberta Health Services, Edmonton, Alberta, Canada.
Anderson W ChuckUniversity of Alberta, Edmonton, Alberta, Canada.
Alberta Health Services · CAInstitute of Health Economics · CAUniversity of Calgary · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a conceptual approach to determine the optimal solution to delivering a health technology, consistent with the objective of maximizing patient outcomes subject to resources available to a publicly funded health system. The article addresses two key policy questions: 1) adding system values through appropriate planning of health services delivery and 2) considering the tradeoff between patient outcomes and costs to the health system through appropriate use of health technologies for conditions with time-dependent treatment outcomes. We develop a health technology optimization framework that considers geographical variation and searches for the best delivery method through a pairwise comparison of all possible strategies, factoring in controlled variables including disease epidemiology, time or distance to hospitals, available medical services, treatment eligibility, treatment efficacy, and costs. Taking variations of these factors into account would help support a more efficient allocation of health resources. Drawing identified strategies together then creates a map of optimal strategies. We apply the proposed method to a policy-relevant health technology assessment of endovascular therapy (EVT) for treating acute ischemic stroke. The best strategy for providing EVT relies on the geographical location of stroke onset and the decision maker's preference for either patient outcomes or economic efficiency. The proposed method produced an optimization map showing the optimal strategy for EVT delivery, which maximizes patient outcomes while minimizing health system costs. In the illustrative case study, there were no tradeoffs between health outcomes and costs, meaning that the delivery strategies that were clinically optimal for patients were also the most cost-effective. In conclusion, the health technology optimization approach is a useful tool for informing implementation decisions and coordinating the delivery of complex health services such as EVT.

Indexed as

cost-effectiveness analysisendovascular therapyhealth system optimizationhealth system planninghealth technology assessmentstroke

Identifiers

PMID30288446
PMCPMC6157433
OpenAlexW2801284994

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.