ReviewHealth care management science2017
Case mix planning in hospitals: a review and future agenda.
Review in Health care management science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Data-driven identification of outpatient-suitable procedures: a machine learning approach.Health care management science · 2026Article
- Analytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring.Healthcare (Basel, Switzerland) · 2024Article
- Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.Health care management science · 2024Article
- Article
- Minimizing patients total clinical condition deterioration in operating theatre departments.Annals of operations research · 2022Article
- Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units.Health care management science · 2022Article
- An Overview of Hospital Capacity Planning and Optimisation.Healthcare (Basel, Switzerland) · 2022Review
- Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.Computational and mathematical methods in medicine · 2021Article
- Analyzing economies of scale and scope in hospitals by use of case mix planning.Health care management science · 2020Article
- Classification of hospital admissions into emergency and elective care: a machine learning approach.Health care management science · 2019Article
- Operations research in intensive care unit management: a literature review.Health care management science · 2018Review
- The operating room case-mix problem under uncertainty and nurses capacity constraints.Health care management science · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital's revenue. The contribution of this article is to provide the first literature review focusing on the case mix planning problem. We describe the problem, distinguish it from similar planning problems, and evaluate the existing literature with regard to problem structure and managerial impact. Further, we identify gaps in the literature. We hope to foster research in the field of case mix planning, which only lately has received growing attention despite its fundamental economic impact on hospitals.
Indexed as
Identifiers
26386970What OpenQuestion holds
Registered trials
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.