Case Mix Tool Research Articles

Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients

BackgroundHospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system.MethodsRetrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity.ResultsThe analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011–5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system.ConclusionsThe Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

Identifying people requiring special care dentistry: A scoping review.

This scoping review aimed to document tools designed to identify persons requiring special care dentistry (SCD) and to provide a better understanding of the factors that justify adaptation in the provision of care. A scoping review methodology was applied. An electronic search was performed in April 2021 using Pubmed and Embase. Additional tools were sought using hand searches and informal professional networking. Ten tools were identified that either predict the complexity of dental management or that retrospectively judge the complexity of care, of which two are as yet unpublished. Some had been developed for a specific population (e.g., patients with Alzheimer's disease, with learning disability, elderly persons) whilst others were applicable for any population (case mix tools). Factors considered included the patient's medical history, ability to cooperate, physical and cognitive autonomy, communication skills, anxiety, need for sedation, oral risk factors, ability to consent and the administrative burden for the dentist. Identifying persons requiring SCD is possible by looking at various factors that influence the provision of dental care. There may be need for adaptation of tools to local circumstances and to the intended usage of the tool at a health services, systems or policy level.

Comparing the BDA case mix tool and simplified case mix tool for stratification of public dental patients with disabilities in South Australia.

Introduction All individuals have a right to equal access to oral healthcare. Finding a dental practitioner experienced in managing individuals with special needs is a commonly reported barrier to accessing oral healthcare for people with disabilities.Aim The aim of this study was to determine the applicability of the British Dental Association (BDA) case mix tool (CMT) and the simplified case mix tool (sCMT) in an Australian context.Methods A retrospective analysis of 131 dental records for patients on recall at the Special Needs Unit, Adelaide Dental Hospital compared the specialist-performed complexity stratification with results obtained using the BDA CMT and sCMT by a general dentist.Results The BDA CMT demonstrated substantial agreement with the specialist-performed complexity stratification and had a higher reliability than the sCMT.Conclusions The BDA CMT may be suitable for use in the Australian context by general dental practitioners to identify the level of complexity of an individual with special needs, so as to ensure their oral healthcare needs are matched to a dental practitioner with the required skills and experience.

Identifying Persons with Special Healthcare Needs in Dentistry-Development and Validation of the French Case Mix Tool.

Providing dental care for certain patient groups is complicated due to difficulties with cooperation, communication, health conditions, and social context, amongst others. The majority of dentists in France work within a public fee-per-item system. A new measure has been introduced providing a financial supplement to dentists for each episode of care for a patient with a severe disability. This supplement is justified by completion of the French Case Mix tool (FCM), a new measure designed to retrospectively identify episodes of dental care that have required adaptation and additional time or expertise. The aim of this study was to investigate the validity and psychometric properties of the FCM. The content validity of the tool was improved at each round of pilot development, involving 392 patient encounters. Test-retest data at 2 weeks for 12 fictional patient treatment episodes were collected from 51 dentists. This phase confirmed inter- and intra-dentist reproducibility, criterion validity, and interpretability. Retrospective analysis of 4814 treatment episodes nationally demonstrated high reliability, internal consistency, and construct validity. Overall, the FCM showed high validity and good psychometric properties. However, the impact of providing a financial supplement on improving access to care for persons with special needs has yet to be evaluated.

Trends in prevalence of chronic disease and multimorbidity in Ontario, Canada.

BACKGROUND:New case-mix tools from the Canadian Institute for Health Information offer a novel way of exploring the prevalence of chronic disease and multimorbidity using diagnostic data. We took a comprehensive approach to determine whether the prevalence of chronic disease and multimorbidity has been rising in Ontario, Canada.METHODS:In this observational study, we applied case-mix methodology to a population-based cohort. We used 10 years of patient-level data (fiscal years 2008/09 to 2017/18) from multiple care settings to compute the rolling 5-year prevalence of 85 chronic diseases and multimorbidity (i.e., the co-occurrence of 2 or more diagnoses). Diseases were further classified based on type and severity. We report both crude and age- and sex-standardized trends.RESULTS:The number of patients with chronic disease increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased 12.2% to 6.5 million. Overall increases from 2008/09 to 2017/18 in the crude prevalence of chronic conditions and multimorbidity were driven by population aging. After adjustments for age and sex, the prevalence of patients with ≥ 1 chronic conditions decreased from 70.2% to 69.1%, and the prevalence of multimorbidity decreased from 47.1% to 45.6%. This downward trend was concentrated in minor and moderate diseases, whereas the prevalence of many major chronic diseases rose, along with instances of extreme multimorbidity (≥ 8 conditions). Age- and sex-standardized resource intensity weights, which reflect relative expected costs associated with patient diagnostic profiles, increased 4.6%.INTERPRETATION:Evidence of an upward trend in the prevalence of chronic disease was mixed. However, the change in case mix toward more serious conditions, along with increasing patient resource intensity weights overall, may portend a future need for population health management and increased health system spending above that predicted by population aging.

New version of case mix tool for special care and paediatric dentistry launched

New version of case mix tool for special care and paediatric dentistry launched

Case-mix tool, costs and effectiveness in improving primary care mental health and substance abuse services

Background: Despite its importance in improving care and developing services, high-quality data evaluating cost-effectiveness and services in different case-mix populations is scarce in primary care.Aims: The objective was to investigate the service use of those mental health and substance abuse patients, who use lots of services.Methods: Primary health care diagnosis-related groups (pDRG) is a tool to evaluate service provider system and improve efficiency, productivity and quality. We viewed all pDRG results available from the year 2015 concerning municipal mental health and substance abuse services.Results: In primary care mental health and substance abuse services, the most common ICD-10-codes were depression and substance abuse. One-fifth of patients produced 57% of costs. Their medium of appointments was 16 per year versus 6 per year of all patients. Only 54% of their diagnoses were recorded in the electronic health records versus 75% of all patients. They made 5.7 different pDRG episodes, including 1.8 episodes of depression, per patient. The average episode cost for this patient group was 301€.Conclusions: pDRG makes health care production transparent also in mental health and substance abuse services. It is easy to identify patients, who use a lot of services and thus induce the majority of costs, and focus on their needs in managing and developing services.

Using the simplified case mix tool (sCMT) to identify cost in special care dental services to support commissioning.

To determine if the sCMT can be used to identify costs of service provision. Patients (n=495) attending the Sussex Community NHS Trust Special Care Dental Service for care were assessed using the sCMT. sCMT score and costs (staffing, laboratory fees, etc.) besides patient age, whether a new patient and use of general anaesthetic/intravenous sedation. Statistical analysis (adjusted linear regression modelling) compared sCMT score and costs then sensitivity analyses of the costings to age, being a new patient and sedation use were undertaken. Regression tables were produced to present estimates of service costs. Costs increased with sCMT total scale and single item values in a predictable manner in all analyses except for 'cooperation'. Costs increased with the use of IV sedation; with each rising level of the sCMT, and with complexity in every sCMT category, except cooperation. Costs increased with increase in complexity of treatment mode as measured by sCMT scores. Measures such as the sCMT can provide predictions of the resource allocations required when commissioning special care dental services.

Care Coordination - How can we measure it?

Introduction : In the definition of primary care put forward by Dr. Barbara Starfield, primary care refers those health-related needs of people “too uncommon to maintain competence and coordinates care when people receive services at other levels of care” (1) Patients with poorly coordinated care are likely to have more costly and lower quality health care due to factors such as excess utilization resulting from redundant investigations, potentially harmful missed drug-disease interactions, and lower patient satisfaction. Therefore, the identification of patients at risk of poor coordination is essential. This presentation will address the impact of information on improving coordination of care across the spectrum of the health care and community services systems. As morbidity burden increases the number of different clinicians seen rises(2), yet coordination of care is threatened when information does not readily flow between those involved in delivering care. Case-mix tools which transform routinely collected electronic health data into actionable information can support both the clinician's decision making process and the policymaker to provide better coordinated care through the exchange of clinical data, measurement of patients’ needs, and a better understanding of the use of healthcare resources. Thus the imperative for coordination requires that all information generated in the care of patients be captured in the care provided over time. Theory / Method : The Johns Hopkins ACG® System has developed four complementary coordination markers as well as a coordination risk score to systematically assess the risk of poor coordination of care. In combination, the markers can identify populations at risk for poor coordination which has implications for cost, quality, and performance assessment. Greater insight about the convergence of risk, medical utilization and prescribing patterns can be captured by combining risk defined by diagnoses with risk defined by pharmacy information. Further, studies have shown that when clinicians share patients with other clinicians more frequently, they are more likely to have referral relationships and seek advice. With the release of Version 11, the ACG System introduced a measure of patient sharing among physicians, termed “Care Density”. This patient-level measure assesses the number of individual clinicians a patient sees and the degree to which those clinicians share other patients. The care density measure is based on the hypothesis that patients seen by clinicians who share patients more frequently have higher levels of communication and information sharing. (3,4) Results : An initial study of the Care Density measure on 9,596 patients with congestive heart failure (CHF) and 52,688 with diabetes (DM) demonstrated a significant correlation between lower inpatient costs and rates of hospitalization amongst those patients with high care density. Also, for diabetic patients with high care density, lower outpatient costs and higher pharmacy costs were found.(3) A more recent study of the Care Density measure expanded its population size to 1.7 million patients with CHF, COPD and/or Diabetes. Among all patients, patients with the highest care density—indicating high levels of patient-sharing among their office-based physicians— had significantly lower rates of adverse events measured as Prevention Quality Indicators (PQIs) compared to patients with low care density (Odds Ratio [OR] 0.88, 95% Confidence Interval [CI] 0.85-0.92). A significant association between care density and PQIs was also observed for patients with DM but not CHF or COPD.(4) Diabetic patients with higher care density scores had significantly lower odds of 30-day readmissions (OR 0.68, 95%CI 0.48-0.97). Significant associations were observed between care density and HEDIS measures though not always in the expected direction.(4) Discussion & Conclusion : Through a better understanding of how patients are shared amongst clinicians, as well as identifying those patients at risk of uncoordinated care, coordination can be improved, rates of hospitalization reduced and potential cost savings achieved. Further research is necessary to substantiate these results in other health care settings.

Assessing sedation need and managing referred dentally anxious patients: is there a role for the Index of Sedation Need?

To conduct an exploratory investigation of public dental service (PDS) practitioners' planned sedation modality using a structural equation modelling approach, in order to identify the explanatory value of using the Index of Sedation Need (IOSN), or its component parts, to predict sedation modality in patients referred with dental anxiety. A convenience sample of patients referred to the PDS for dental anxiety management was invited to take part. The IOSN was completed for each patient (patient dental anxiety, medical and behavioural indicators and dental treatment complexity) as well as the American Society of Anesthesiologists Physical Status Classification System and the Case Mix Tool. The practitioners completed details of their planned sedation modality and identified normative dental treatment need. The data were entered onto an SPSS v21 database and subjected to frequency distributions, t-tests, correlation analysis and exploratory partial structural equation modelling (SEM). Ninety-five percent of patients were ranked as MDAS 3 or 4, indicating high dental anxiety; 69% had a medical condition, which might impact on dental treatment and 82% had a dental treatment need, which was classified as intermediate/complex according to the IOSN. Eighty-eight percent of the patients in accordance with the IOSN required sedation: 62% of patients were assessed as requiring intravenous sedation. The IOSN discriminated between patients who were assessed as requiring more complex sedation modalities and had a greater normative treatment need. The SEM showed that the patient dental anxiety (P <0.02) and dental treatment complexity (P <0.02) predicted planned sedation modality. Functional morbidity was less strong, as a predictor, and was significant at the ten percent level. The IOSN is a useful and valid assessment of sedation need and predicted sedation modality for patients referred with high dental anxiety states and secondly, that component parts of the IOSN add explanatory value in practitioners' choice of planned sedation modality.

Weighing up the weighted case mix tool (WCMT): a psychometric investigation using confirmatory factor analysis.

To assess the use of the WCMT in two Scottish health boards and to consider the impact of simplifying the tool to improve efficient use. A retrospective analysis of routine WCMT data (47,276 cases). Public Dental Service (PDS) within NHS Lothian and Highland. The WCMT consists of six criteria. Each criterion is measured independently on a four-point scale to assess patient complexity and the dental care for the disabled/impaired patient. Psychometric analyses on the data-set were conducted. Conventional internal consistency coefficients were calculated. Latent variable modelling was performed to assess the 'fit' of the raw data to a pre-specified measurement model. A Confirmatory Factor Analysis (CFA) was used to test three potential changes to the existing WCMT that included, the removal of the oral risk factor question, the removal of original weightings for scoring the Tool, and collapsing the 4-point rating scale to three categories. The removal of the oral risk factor question had little impact on the reliability of the proposed simplified CMT to discriminate between levels of patient complexity. The removal of weighting and collapsing each item's rating scale to three categories had limited impact on reliability of the revised tool. The CFA analysis provided strong evidence that a new, proposed simplified Case Mix Tool (sCMT) would operate closely to the pre-specified measurement model (the WMCT). A modified sCMT can demonstrate, without reducing reliability, a useful measure of the complexity of patient care. The proposed sCMT may be implemented within primary care dentistry to record patient complexity as part of an oral health assessment.

ICF and casemix models for healthcare funding: use of the WHO family of classifications to improve casemix

Purpose: Casemix models for funding activity in health care and assessing performance depend on data based on uniformity of resource utilisation. It has long been an ideal to relate the measure of value more to patient outcome than output. A problem frequently expressed by clinicians is that measures of activity such as Functional Independence Measure (FIM) and Barthel Index scores may not sufficiently represent the aspirations of patients in many care programs. Method: Firstly, the key features of the International Classification of Functioning, Disability and Health are outlined. Secondly, the use of ICF dimensions in Australia and other countries is reviewed. Thirdly, a broader set of domains with potential for casemix funding models and performance reporting is considered. Results: In recent years, the ICF has provided a more developed set of domains against which outcome goals can be expressed. Additional dimensions could be used to supplement existing data. Instances of developments in this area are identified and their potential discussed. Conclusions: A well-selected set of data items representing the broader dimensions of outcome goals may provide the ability to more meaningfully and systematically measure the goals of both curative and rehabilitation care against which outcome should be measured. More information about patient goals may be needed.Implications for RehabilitationCasemix models for funding and outcome analysis of healthcare rely on accurate and complete data to classify the complexity and costliness of the care required.The International Classification of Functioning, Disability and Health – ICF has been developed to provide better developed domains of function that include activity and participation. Goals of the patient are an important aspect of the participation domains.Inclusion of activity and participation domains in casemix analysis is likely to improve the performance of casemix tools and better categorise care needs and resource requirements of particular rehabilitation case types.

International casemix and funding models: lessons for rehabilitation

This series of articles for rehabilitation in practice aims to cover a knowledge element of the rehabilitation medicine curriculum. Nevertheless they are intended to be of interest to a multidisciplinary audience. The competency addressed in this article is ‘An understanding of the different international models for funding of health care services and casemix systems, as exemplified by those in the US, Australia and the UK.’ Background: Payment for treatment in healthcare systems around the world is increasingly based on fixed tariff models to drive up efficiency and contain costs. Casemix classifications, however, must account adequately for the resource implications of varying case complexity. Rehabilitation poses some particular challenges for casemix development. Objective: The objectives of this educational narrative review are (a) to provide an overview of the development of casemix in rehabilitation, (b) to describe key characteristics of some well-established casemix and payment models in operation around the world and (c) to explore opportunities for future development arising from the lessons learned. Results: Diagnosis alone does not adequately describe cost variation in rehabilitation. Functional dependency is considered a better cost indicator, and casemix classifications for inpatient rehabilitation in the United States and Australia rely on the Functional Independence Measure (FIM). Fixed episode-based prospective payment systems are shown to contain costs, but at the expense of poorer functional outcomes. More sophisticated models incorporating a mixture of episode and weighted per diem rates may offer greater flexibility to optimize outcome, while still providing incentive for throughput. Conclusion: The development of casemix in rehabilitation poses similar challenges for healthcare systems all around the world. Well-established casemix systems in the United States and Australia have afforded valuable lessons for other countries to learn from, but have not provided all the answers. A range of casemix and payment models is required to cater for different healthcare cultures, and casemix tools must capture all the key cost-determinants of treatment for patients with complex needs.

BDA special care case mix model

Routine dental care provided in special care dentistry is complicated by patient specific factors which increase the time taken and costs of treatment. The BDA have developed and conducted a field trial of a case mix tool to measure this complexity. For each episode of care the case mix tool assesses the following on a four point scale: 'ability to communicate', 'ability to cooperate', 'medical status', 'oral risk factors', 'access to oral care' and 'legal and ethical barriers to care'. The tool is reported to be easy to use and captures sufficient detail to discriminate between types of service and special care dentistry provided. It offers potential as a simple to use and clinically relevant source of performance management and commissioning data. This paper describes the model, demonstrates how it is currently being used, and considers future developments in its use.

Seven modes of healthcare operations - a tool for casemix analysis?

Seven modes of healthcare operations - a tool for casemix analysis?

Access to Clinically-Detailed Patient Information

Although we also value service and ethical aspects of healthcare provision, the main reason we tolerate spending $2 trillion annually on healthcare (almost $7000 per person)1 is with the hope that the care we receive will improve the quality and length of our lives. Given the overwhelming evidence that much of healthcare spending is wasteful and that quality problems are pervasive, it is more than sensible to demand greater accountability from providers, and what better way than to directly assess the outcomes in which we are most interested, such as risk-adjusted mortality. As early as the 1910s, E. A. Codman called for just such accountability.2 Although his ideas were largely ignored for many years, since the 1980s there have been repeated attempts to implement risk-adjusted outcomes as accountability measures, with improving the completeness and affordability of case-mix adjustment widely being seen as the key to success. In this issue of Medical Care, Escobar et al3 report on how integrating automated data across medical information systems can produce a risk-adjustment tool for hospital mortality that is inexpensive and highly predictive. This work builds on that by Render et al4 and provides a case-mix tool that has substantial implications for quality monitoring and improvement, and is likely to also be a valuable research tool. However, performance measurement leaders would be wise to heed the authors' warning3 sample size limitations usually limit the accuracy of risk-adjusted mortality rates as an accountability measure even if perfect case-mix was obtainable,56 something that is easily overlooked unless statistical methods that adjust for measurement error are used.7'8 In addition, even an impressive prediction tool, like that developed by the authors, has been shown in past research to be unable to reliably account for high levels of patient selection (ie, that often seen at referral centers or in transfer patients).9 However, risk-adjusted outcome rates have proved to be extremely valuable in cooperative quality improvement (QI) programs in which members are required to disclose and discuss their outcomes and are encouraged to learn from others in the program. Such programs have been very successful where implemented101 !; however, the dissemination of such programs has often been limited by the resources, time, and costs of obtaining the clinical data needed for legitimate case-mix adjustment. The work by investigators like Escobar et al, Render et al, and others, however, has implications well beyond risk-adjusted mortality rates. This research is just one of many examples of how effective administrative initiatives to improve the efficiency and quality of health care usually require access to clinically-detailed medical information. Kerr and colleagues have demonstrated how linking multiple automated data sources in an integrated healthcare system can greatly improve the accuracy of and clinical insights into processes and appropriateness of care, such as the appropriateness of intensification of blood pressure and lipid therapies.12 Further work has demonstrated how more comprehensive, clinically-detailed chart-based quality measurement tools can be used to effec-

The Australian National Sub-acute and Non-acute Patient Casemix Classification (AN-SNAP): its application and value in a stroke rehabilitation programme.

To investigate whether the Australian National Sub-acute and Non-acute Patient Casemix Classification (SNAP) and Functional Independence Measure and Functional Related Group (Version 2) (FIM-FRG2) casemix systems can be used to predict functional outcome, and reduce the variance of length of stay (LOS) of patients undergoing rehabilitation after strokes. The study comprised a retrospective analysis of the records of patients admitted to the Cedar Court Healthsouth Rehabilitation Hospital for rehabilitation after stroke. The sample included 547 patients (83.3% of those admitted with stroke during this period). Patient data were stratified for analysis into the five SNAP or nine FIM-FRG2 groups, on the basis of the admission FIM scores and age. The AN-SNAP classification accounted for a 30.7% reduction of the variance of LOS, and 44.2% of motor FIM, and the FIM-FRG2 accounts for 33.5% and 56.4% reduction respectively. Comparison of the Cedar Court with the national AN-SNAP data showed differences in the LOS and functional outcomes of older, severely disabled patients. Intensive rehabilitation in selected patients of this type appears to have positive effects, albeit with a slightly longer period of inpatient rehabilitation. Casemix classifications can be powerful management tools. Although FIM-FRG2 accounts for more reduction in variance than SNAP, division into nine groups meant that some contained few subjects. This paper supports the introduction of AN-SNAP as the standard casemix tool for rehabilitation in Australia, which will hopefully lead to rational, adequate funding of the rehabilitation phase of care.

DRGs and inequities among French hospitals: from a new dilemma to another health care policy.

The unpredictable French health care reform of 1995-96 introduced casemix tools to fund French hospitals at a regional level. After two years (1997-98), health care authorities and hospitals are facing great inequities and inefficiencies (1 to 3) in 22 French regions. Only one region (Ile de France, Paris) is above the national casemix index mean, the Index Synthétique d' Activité, and very few are equal to this national mean. The dilemma for most of the 22 regions under this national mean, and within a region like Rhône-Alpes (Saint Etienne), with inequities from 1 to 2, is to decide whether the main goal of the new health care policy is to reduce inequities and inefficiencies.

Book Review: Case Mix Tools for Decision Making in Health Care

Book Review: Case Mix Tools for Decision Making in Health Care

Ambulatory Case Mix Funding Systems in Canada

The implementation of inpatient case mix funding in Alberta and Ontario does not allow for adequate incentives to shift resources to an outpatient basis, where appropriate, or to provide outpatient care efficiently. This paper explores the prospects and problems of further extending case mix tools into this area. The availability of tools to characterize output for day surgery, special clinics and emergency care is surveyed. We conclude that case mix funding is desirable and feasible for ambulatory surgery; however, it is questionable for emergency care and special clinics. However, developments in this area in the United States will continue, and this will likely maintain an interest in Canada.