RESEARCH DISCIPLINE
Health Services Management Analysis
Hospital Data With Their Structure
For studies of hospitals, units and health systems: performance measures analyzed with their nesting and time trends, case mix adjusted, and comparisons between organizations reported with intervals so that rank is not mistaken for quality.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does Statistical Analysis in Healthcare Management Involve?
Health services management analysis covers performance and operational data in which patients are nested within units, units within hospitals and hospitals within systems, and in which measures change over time. It covers risk adjustment of outcomes, models that respect the nesting, the analysis of trends and program changes, and the reporting of comparisons between organizations with their uncertainty. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, supports health services managers, administrators and graduate researchers: an in-house PhD-level statistician models your administrative data with their nesting in view and sets out what a comparison of organizations can legitimately show. The managerial conclusions, and any recommendation built on them, belong to you.
WHEN TO ENGAGE
Decisions Where Hospital Data Need Careful Analysis
Organizational data tempt researchers into comparisons they cannot always bear, so review helps most when measures are chosen, units compared, trends read or a board asks questions.
Measures to Define Quality, cost or throughput measures are being chosen, and the numerator, denominator, risk adjustment and unit of analysis must be settled before any data are extracted.
Units to Compare Hospitals, wards or clinics are to be compared on an outcome, so case mix, small volumes and clustering must be handled before a difference between them is interpreted.
Change Over Time A policy, a staffing change or a program began on a known date, and the earlier trend, seasonality and concurrent changes shape what can be concluded from the later data.
Committee or Board Questions A doctoral committee, hospital board or journal doubts a ranking, a comparison or a causal claim drawn from administrative data, and the analysis has to show what still holds.
WHAT THE SUPPORT COVERS
Statistical Components of Health Services Management Research
The analysis works with organizations as well as patients, so a study moves from defining its measures to comparing units fairly.
Measures and Data Structure
Administrative extracts are examined for definitions, coding changes and missing fields, and each performance measure is specified with its numerator, denominator and exclusions.
Risk Adjustment
Outcomes are adjusted for patient case mix with a model suited to the outcome, and the adjustment is checked before any unit is compared with another.
Nested Data Models
Patients within wards and hospitals are modeled with Linear Mixed Models (Multilevel Modeling), separating variation between units from variation within them.
Trends and Program Effects
Staffing, policy or program changes are evaluated with Interrupted Time Series on monthly unit data, separating a change after the start date from seasonality and the trend already under way.
Comparing Organizations
Hospital performance data analysis reports differences with intervals and shrinkage for small units, so that apparent outliers are told apart from chance variation.
Management Research Reporting
Results are reported under STROBE or SQUIRE 2.0 as the design requires, in line with Health Sciences research and Business Administration & Management.
COMMON ISSUES
Pitfalls in Comparing Hospitals and Units
Comparisons between organizations are easy to compute and hard to defend, and the six problems below decide whether a ranking or a program effect survives scrutiny.
Ranks Without Intervals Hospitals are ranked on a rate, though small volumes make ranks unstable, and a unit near the top this year may sit near the middle next year by chance alone.
Case Mix Ignored Raw outcome rates are compared across hospitals that treat different patients, so differences in severity are read as differences in the quality of care.
Patients Treated as Independent Patients in one ward share staff and practice, and a standard error that ignores this clustering is too small, producing intervals that are too narrow.
Before and After Without a Trend A measure is compared before and after a program with two averages, though a trend that was already improving would have produced the same difference.
Worst Performers Regress Units chosen for intervention because they performed worst tend to improve even without one, and that improvement is credited to the program.
Measures Shaped by Coding A change in coding practice or in the definition of a measure is read as a change in performance, so each series needs a check for breaks in its definition.
DELIVERABLES
What You Receive for Your Management Study
These materials let another analyst, a board or an examiner reproduce each figure from the administrative source and see exactly how units were compared.
Measure Specification Sheet A sheet defining each measure with its data source, numerator, denominator, exclusions and period, so that another analyst can reproduce the figures.
Linked Analysis Dataset Patient, unit and hospital files linked and cleaned, with a codebook and a record of every exclusion and each correction made to the extract.
Risk-Adjustment Summary The case mix model, its predictors and its performance, so that a reader can judge how well differences between units were adjusted.
Trend and Comparison Tables Time series figures, model estimates and comparison tables with 95% confidence intervals, formatted for a board report, a thesis or a journal.
Funnel and Caterpillar Plots Plots of unit performance against volume or rank with their intervals, showing which units differ from the whole by more than chance.
Decision Limits Memo A short memo, with the analysis code attached, on how far each result can support a staffing, resourcing or program decision and which questions the administrative data cannot answer.
ILLUSTRATIVE EXAMPLE
Discharge Coordination Across a Regional System
An illustration of how a program evaluated across several hospitals is analyzed when coding practice changed during the study.
ILLUSTRATIVE EXAMPLE · HEALTH SYSTEM PERFORMANCE RESEARCH · UNITED STATES
A doctoral candidate in health services management wanted to know whether a discharge coordination program shortened length of stay in the hospitals of one regional system.
Hospitals adopted the program in different months, so the plan used patient-level data nested within hospitals, an event-study model comparing each hospital before and after its own start month with hospitals not yet using the program, and a case mix adjustment fixed in advance. Two hospitals, however, changed how they coded admission type during the period, which would have produced a spurious shift in length of stay. The affected series were examined, the definition was harmonized where the records allowed, and a sensitivity analysis excluded the two hospitals. The program effect was reported as an association within this system, with other concurrent changes discussed.
Outcome: The candidate received the adjusted change in length of stay with its 95% confidence interval, the hospital-level plots, the sensitivity analysis, the measure specification and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Working From Your Data Warehouse
Hospital extracts often arrive as SAS files or flat exports, and the analysis is set up in a package your department can maintain after delivery.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
Statistical Support Within Your Own Management Research
The statistical treatment of your organizational data is the part OnlineSPSS supports, and the management reasoning and any recommendation to a board remain your own.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your research. Authorship of the dissertation, report or article remains yours.
Your Research Decisions The research question, the management interpretation, the discussion and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Questions About Healthcare Management Analysis
Fair rankings, programs adopted everywhere at once and the link between staffing and patient outcomes are the questions this kind of study tends to raise.
What does management analysis of health services cover?
Health services management analysis covers performance measures, risk adjustment, models for patients nested in units and hospitals, trends and program changes, and comparisons reported with their uncertainty. Cost, length of stay and staffing outcomes are included, with models suited to skewed or count data.
Can hospitals be ranked fairly on one outcome measure?
Rankings are fragile. Hospitals differ in case mix and volume, and small units show large random swings. Adjusting for case mix, reporting intervals and using funnel plots or shrinkage show which units differ beyond chance, and a rank is usually less informative than the interval around it.
What can be concluded if every hospital adopted a program at once?
A before and after comparison with no control can describe a change in level or trend but cannot exclude other changes at the same time. Modeling the earlier trend, checking seasonality and looking for a comparison series strengthen the inference, and the wording stays careful about cause.
Can staffing levels be linked to patient outcomes from administrative data?
An association can be estimated, with care. Staffing often rises with patient acuity, so sicker wards may show more staff and worse outcomes at the same time. Measuring staffing by shift or day, adjusting for acuity and describing the result as an association make the analysis defensible, and reverse causation is discussed openly.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as a proposal, measure definitions or an administrative extract, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Compare Units
on a Fair Footing
Describe the hospitals or units, the measures and the period your extract covers, with any board or committee questions, and you will hear how a fair comparison of them could be built.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014