Evidence map›Paper›PMID 25038609›Full record

ArticleBMC health services research2014

Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies.

Richard J Lilford, Alan J Girling, Aziz Sheikh, Jamie J Coleman, Peter J Chilton, Samantha L Burn, David J Jenkinson, Laurence Blake, Karla Hemming

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2014. 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
4.0field-weighted citation impact, top 6% 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, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. HEADROOM APPROACH TO DEVICE DEVELOPMENT: CURRENT AND FUTURE DIRECTIONS.International journal of technology assessment in health care · 2015
    Article
  5. Review
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

9 authors at 3 institutions in 2 countries.

Richard J LilfordWarwick Medical School, University of Warwick, Coventry CV4 7AL, UK. r.j.lilford@warwick.ac.uk.
Alan J Girling
Aziz Sheikh
Jamie J Coleman
Peter J Chilton
Samantha L Burn
David J Jenkinson
Laurence Blake
Karla Hemming
University of Birmingham · GBUniversity of Warwick · GBHarvard University · US

Funding

Department of Health RP-PG-1209-10099Department of Health WMCLAHRC-2014-1Medical Research Council G0800808
6 · The paper itself

Abstract

backgroundThis protocol concerns the assessment of cost-effectiveness of hospital health information technology (HIT) in four hospitals. Two of these hospitals are acquiring ePrescribing systems incorporating extensive decision support, while the other two will implement systems incorporating more basic clinical algorithms. Implementation of an ePrescribing system will have diffuse effects over myriad clinical processes, so the protocol has to deal with a large amount of information collected at various 'levels' across the system. METHODS/

designThe method we propose is use of Bayesian ideas as a philosophical guide.Assessment of cost-effectiveness requires a number of parameters in order to measure incremental cost utility or benefit - the effectiveness of the intervention in reducing frequency of preventable adverse events; utilities for these adverse events; costs of HIT systems; and cost consequences of adverse events averted. There is no single end-point that adequately and unproblematically captures the effectiveness of the intervention; we therefore plan to observe changes in error rates and adverse events in four error categories (death, permanent disability, moderate disability, minimal effect). For each category we will elicit and pool subjective probability densities from experts for reductions in adverse events, resulting from deployment of the intervention in a hospital with extensive decision support. The experts will have been briefed with quantitative and qualitative data from the study and external data sources prior to elicitation. Following this, there will be a process of deliberative dialogues so that experts can "re-calibrate" their subjective probability estimates. The consolidated densities assembled from the repeat elicitation exercise will then be used to populate a health economic model, along with salient utilities. The credible limits from these densities can define thresholds for sensitivity analyses. DISCUSSION: The protocol we present here was designed for evaluation of ePrescribing systems. However, the methodology we propose could be used whenever research cannot provide a direct and unbiased measure of comparative effectiveness.

Indexed as

Models, EconomicTechnology Assessment, BiomedicalAlgorithmsBayes TheoremCost-Benefit AnalysisElectronic PrescribingHumansMedication ErrorsQuality-Adjusted Life YearsQuality of Life

Identifiers

PMID25038609
PMCPMC4118257
OpenAlexW2107658405

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.