Evidence map›Paper›PMID 31185989›Full record

ArticleBMC health services research2019

Decision making tools for managing waiting times and treatment rates in elective surgery.

Daniel Adrian Lungu, Tommaso Grillo Ruggieri, Sabina Nuti

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
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  8. Observational
  9. The Impact of New Surgical Techniques on Geographical Unwarranted Variation: The Case of Benign Hysterectomy.International journal of environmental research and public health · 2021
    Article
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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

3 authors at 1 institution in 1 country.

Daniel Adrian LunguHealthcare Management Laboratory, Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, Pisa, Italy. danieladrian.lungu@santannapisa.it.ORCID http://orcid.org/0000-0002-8612-8384
Tommaso Grillo RuggieriHealthcare Management Laboratory, Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, Pisa, Italy.
Sabina NutiHealthcare Management Laboratory, Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, Pisa, Italy.
Scuola Superiore Sant'Anna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWaiting times for elective treatments, including elective surgery, are a source of public concern and therefore are on policy makers' agenda. The long waiting times have often been tackled through the allocation of additional resources, in an attempt to reduce them, but results are not straightforward. At the same time, researchers have reported wide geographical variations in the provision of elective care not driven by patient needs or preferences but by other factors. The paper analyses the relationship between waiting times and treatment rates for nine high-volume elective surgical procedures in order to support decision making regarding the availability of these services for the citizens. Using the framework already proposed for the diagnostic services, we identify different patterns that can be followed to align the supply with patient needs in the Italian context.

methodsAfter measuring the waiting times and the treatment rates for nine procedures in the 34 districts in Tuscany, we performed correlation analyses. Then, we plotted the results in a matrix cross-checking waiting times and rates. By doing so, we identified four different contexts that require a second step analysis to tackle unwarranted geographical variations and ensure timely care to patients. Finally, for each district and elective surgical procedure, we measured the economic impact of the different treatment rates in order to evaluate whether there are any supply criticalities and eventually some room for maneuver. We also included active and passive mobility of patients.

resultsThe results show a high degree of variation both in treatment rates and waiting times, especially for the orthopaedic procedures: knee replacement, knee arthroscopy and hip replacement. The analysis performed for the nine interventions shows that the 34 districts are in varying positions in the waiting time-treatment rate matrix, suggesting that there is no straightforward relationship between rates and waiting times. Each combination in the matrix may have different determinants that require healthcare managers to adopt diversified strategies. The decision making process needs to be supported by a two-level analysis: the first one to put in place the matrix that cross-checks waiting times and treatment rates, the second one to analyse the characteristics of each quadrant and the improvement actions that can be proposed.

conclusionsIn Italy, waiting times in elective surgical services are a main policy issue with a relevant geographical variation. Our analysis reveals that this variation is due to multiple elements. In order to avoid simplistic approaches that do not solve the problem but often lead to increased expenditure, policy makers and healthcare managers should follow a two-step strategy firstly identifying the type of context and secondly analysing the impact of elements such as resource productivity, resource availability, patients' preferences and care appropriateness. Only in some cases it is required to increase the service supply.

Indexed as

Clinical Decision-MakingElective Surgical ProceduresWaiting ListsDelivery of Health CareFemaleHumansMaleNeeds AssessmentPolicy MakingDecision makingElective surgeryUnwarranted variationUse ratesWaiting times

Identifiers

PMID31185989
PMCPMC6560774
OpenAlexW2952047602

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.