Evidence map›Paper›PMID 24888268›Full record

ArticleHealth care management science2016

Should inpatients be adjusted by their complexity and severity for efficiency assessment? Evidence from Portugal.

Diogo Cunha Ferreira, Rui Cunha Marques

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Article in Health care management science, 2016. 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
–field-weighted citation impact
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.

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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

2 authors.

Diogo Cunha FerreiraInstituto Superior Técnico (IST), University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal. diogo.cunha.ferreira@tecnico.ulisboa.pt.
Rui Cunha MarquesCentre for Urban and Regional Systems (CESUR), DECivil-IST, University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hospital efficiency analysis depends largely on the model specifications. This study discusses the importance of the case-mix index (CMI) to homogenize the sample of inpatient discharges. It proposes a new index where they are classified by service, since it is usual to have lack of data to compute the CMI and this can influence the credibility of results. Data from the Portuguese national diagnosis-related group (DRG) database was utilized. Three different approaches are developed in this paper, based on locally convex order-m method as well as on translog functions. The first one correlates the efficiency with different inpatients weighting schemes, by using the Nadaraya-Watson method. The second approach compares different frontiers that have been computed using the different weighting schemes. Finally, by using bootstrap, the paper investigates whether the inclusion of severity/ complexity-related variables in the model statistically modifies the results. It has been shown that, under the Portuguese healthcare framework, if the model is environment corrected (which should include epidemiological and main political/ structural health reforms variables), then the severity adjustment of inpatients is pointless. The employment of an inpatient-weighting scheme, such as the CMI, may introduce significant frontier shift, thus its absence is not recommended in productivity evolution analyzes. The CMI shifts the efficiency frontier, but not the relative position of units against it (the last scenario if exogenous variables are present).

Indexed as

Models, TheoreticalCosts and Cost AnalysisData Interpretation, StatisticalDiagnosis-Related GroupsEfficiency, OrganizationalHealth Facility MergerHospital AdministrationHumansLength of StayPortugalResidence CharacteristicsSeverity of Illness IndexCase-mix indexInternment services efficiencyLocally convex order-m methodService-mix indexTranslog function

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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.