Evaluation of the performance of diagnosis-related groups and similar casemix systems: methodological issues

Construction and application of a nursing human resource allocation model based on the case mix index

BACKGROUND

The case mix index (CMI) may reflect the severity of disease and the difficulty of care objectively, and is expected to be an ideal indicator for assessing the nursing workload. The purpose of this study was to explore the quantitative relationship between daily nursing worktime (DNW) and CMI to provide a method for the rational allocation of nursing human resources.

METHODS

Two hundred and seventy-one inpatients and 36 nurses of the department of hepatobiliary surgery were prospectively included consecutively from August to September 2022. The DNW of each patient were accurately measured, and the CMI data of each patient were extracted. Among 10 curve estimations, the optimal quantitative model was selected for constructing the nursing human resource allocation model. Finally, the applicability of the allocation model was preliminarily assessed by analyzing the relationship between the relative gap in nursing human resources and patient satisfaction, as well as the incidence of adverse events in 17 clinical departments.

RESULTS

The median (P25, P75) CMI of the 271 inpatients was 2.62 (0.92, 4.07), which varied by disease type (F = 3028.456, P < 0.001), but not by patient gender (F = 0.481, P = 0.488), age (F = 2.922, P = 0.089), or level of care (F = 0.096, P = 0.757). The median (P25, P75) direct and indirect DNW were 76.07 (57.98, 98.85) min and 43.42 (39.42, 46.72) min, respectively. Among the 10 bivariate models, the quadratic model established the optimal quantitative relationship between CMI and DNW; DNW = 92.3 + 4.8*CMI + 2.4*CMI2 (R2 = 0.627, F = 225.1, p < 0.001). The relative gap between theoretical and actual nurse staffing in the 17 clinical departments were linearly associated with both patient satisfaction (r = 0.653, P = 0.006) and incidence of adverse events (r = - 0.567, P = 0.021). However, after adjusting for other factors, it was partially correlated only with patient satisfaction (rpartial = 0.636, P = 0.026).

CONCLUSION

The DNW derived from CMI can be used to allocate nursing human resources in a rational and convenient way, improving patient satisfaction while ensuring quality and safety.

Patient preferences and attitudes towards first choice medical services in Shenzhen, China: a cross-sectional study

OBJECTIVE

This study aimed to explore the characteristics of Shenzhen residents' preferences and influencing factors regarding their first choice of medical institution at various medical levels, and to understand their attitudes towards community health services.

DESIGN

Cross-sectional survey.

PARTICIPANTS

A total of 1612 participants at least 18 years of age were randomly sampled with stratification among 10 districts in Shenzhen. Data were gathered through a self-designed questionnaire. The effective questionnaire response rate was 93.05%. All patients participated in the study voluntarily, provided written informed consent and were able to complete the questionnaire.

MAIN OUTCOME MEASURES

We measured and compared the participants' expected and actual preferences and influencing factors regarding their first choice of medical service at various medical levels.

RESULTS

More than 50% of the participants preferred municipal and district hospitals as their first choice, and 27.5% chose medical institutions according to specific circumstances. Univariate analysis indicated that age, education, income, medical insurance, housing conditions and registered permanent residence were significantly associated with the actual and expected preferred first medical institution. The main factors influencing participants' actual and expected preferred medical institution differed. With the actual preferred first medical institution as the dependent variable, education, monthly income, medical technology, convenience and providers' service attitude and medical ethics were the main factors (χ²=212.63, p<0.001), whereas with the expected preferred first medical institution as the dependent variable, occupation, Shenzhen registered permanent residence, education and medical technology were the main factors (χ²=78.101, p<0.001).

CONCLUSION

The main factors influencing participants' preferred medical institution and their actual first visit differed. Patients with high education or income or registered permanent residence preferred high-level medical institutions for the first visit.

Describing Serbian Hospital Activity Using Australian Refined Diagnosis Related Groups: A Case Study in Vojvodina Province

Introduction

AR-DRG system for classification hospital episodes was implemented in Serbia to improve efficiency and transparency in the health system.

Methods

L3H3, IQR, and 10th-95th percentile methods were used to identify outlier episodes in the classification. Classification efficiency and within-group homogeneity were measured by an adjusted reduction in variance (R2) and a coefficient of variation (CV).

Results

There were 246,131 hospital episodes with a total 1,651,913 bed days from 14 hospitals. All episodes were classified into 652 groups of which 441 had CV lower than 100%. "Medical groups" accounted for 51% of groups and for 72% of episodes. Chemotherapy and vaginal delivery were the highest volume groups, with 5% and 4% of total episodes. Major diagnostic category 6 (MDC 6, Diseases of the digestive system) was the highest volume MDC, accounting for 11% of episodes. "Day-cases" and "prolonged hospitalisation" accounted for 21% and 3% of episodes, respectively. The average length of stay varied from 5.6 to 8.2 days. Adjusted R2 was 0.3 for untrimmed data. Trimming by L3H3, IQR, and 10th-95th percentile method improved the value of adjusted R2 to 0.61, 0.49, and 0.51, identifying 24%, 7%, and 7% of total cases as outliers, respectively. Mental diseases (MDC 19) remained the lowest adjusted R2 in untrimmed and trimmed datasets.

Conclusion

A long length of stay and a small percentage of "day-cases" characterized hospital activity in Vojvodina. Trimming methods significantly improved DRG efficiency. Future studies should consider cost data.

Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

AIM

To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP) and build up an area-specified senile cataract diagnosis related group (DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.

METHODS

The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.

RESULTS

The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.

CONCLUSION

The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

Potential loss of revenue due to errors in clinical coding during the implementation of the Malaysia diagnosis related group (MY-DRG®) Casemix system in a teaching hospital in Malaysia

BACKGROUND

The accuracy of clinical coding is crucial in the assignment of Diagnosis Related Groups (DRGs) codes, especially if the hospital is using Casemix System as a tool for resource allocations and efficiency monitoring. The aim of this study was to estimate the potential loss of income due to an error in clinical coding during the implementation of the Malaysia Diagnosis Related Group (MY-DRG^®) Casemix System in a teaching hospital in Malaysia.

METHODS

Four hundred and sixty-four (464) coded medical records were selected, re-examined and re-coded by an independent senior coder (ISC). This ISC re-examined and re-coded the error code that was originally entered by the hospital coders. The pre- and post-coding results were compared, and if there was any disagreement, the codes by the ISC were considered the accurate codes. The cases were then re-grouped using a MY-DRG^® grouper to assess and compare the changes in the DRG assignment and the hospital tariff assignment. The outcomes were then verified by a casemix expert.

RESULTS

Coding errors were found in 89.4% (415/424) of the selected patient medical records. Coding errors in secondary diagnoses were the highest, at 81.3% (377/464), followed by secondary procedures at 58.2% (270/464), principal procedures of 50.9% (236/464) and primary diagnoses at 49.8% (231/464), respectively. The coding errors resulted in the assignment of different MY-DRG^® codes in 74.0% (307/415) of the cases. From this result, 52.1% (160/307) of the cases had a lower assigned hospital tariff. In total, the potential loss of income due to changes in the assignment of the MY-DRG^® code was RM654,303.91.

CONCLUSIONS

The quality of coding is a crucial aspect in implementing casemix systems. Intensive re-training and the close monitoring of coder performance in the hospital should be performed to prevent the potential loss of hospital income.

Adaptation of the Acute Organ Failure Score for Use in a Medicare Population

OBJECTIVES

Without widely available physiologic data, a need exists for ICU risk adjustment methods that can be applied to administrative data. We sought to expand the generalizability of the Acute Organ Failure Score by adapting it to a commonly used administrative database.

DESIGN

Retrospective cohort study.

SETTING

One hundred fifty-one hospitals in Pennsylvania.

PATIENTS

A total of 90,733 ICU admissions among 77,040 unique patients between January 1, 2009, and December 1, 2009, in the Medicare Provider Analysis and Review database.

MEASUREMENTS AND MAIN RESULTS

We used multivariable logistic regression on a random split cohort to predict 30-day mortality, and to examine the impact of using different comorbidity measures in the model and adding historical claims data. Overall 30-day mortality was 17.6%. In the validation cohort, using the original Acute Organ Failure Score model's β coefficients resulted in poor discrimination (C-statistic, 0.644; 95% CI, 0.639-0.649). The model's C-statistic improved to 0.721 (95% CI, 0.711-0.730) when the Medicare cohort was used to recalibrate the β coefficients. Model discrimination improved further when comorbidity was expressed as the COmorbidity Point Score 2 (C-statistic, 0.737; 95% CI, 0.728-0.747; p < 0.001) or the Elixhauser index (C-statistic, 0.748; 95% CI, 0.739-0.757) instead of the Charlson index. Adding historical claims data increased the number of comorbidities identified, but did not enhance model performance.

CONCLUSIONS

Modification of the Acute Organ Failure Score resulted in good model discrimination among a diverse population regardless of comorbidity measure used. This study expands the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting using standard administrative data.

Hierarchical Medical System Based on Big Data and Mobile Internet: A New Strategic Choice in Health Care

China is setting up a hierarchical medical system to solve the problems of biased resource allocation and high patient flows to large hospitals. The development of big data and mobile Internet technology provides a new perspective for the establishment of hierarchical medical system. This viewpoint discusses the challenges with the hierarchical medical system in China and how big data and mobile Internet can be used to mitigate these challenges.

Capturing patients' needs in casemix: a systematic literature review on the value of adding functioning information in reimbursement systems

BACKGROUND

Contemporary casemix systems for health services need to ensure that payment rates adequately account for actual resource consumption based on patients' needs for services. It has been argued that functioning information, as one important determinant of health service provision and resource use, should be taken into account when developing casemix systems. However, there has to date been little systematic collation of the evidence on the extent to which the addition of functioning information into existing casemix systems adds value to those systems with regard to the predictive power and resource variation explained by the groupings of these systems. Thus, the objective of this research was to examine the value of adding functioning information into casemix systems with respect to the prediction of resource use as measured by costs and length of stay.

METHODS

A systematic literature review was performed. Peer-reviewed studies, published before May 2014 were retrieved from CINAHL, EconLit, Embase, JSTOR, PubMed and Sociological Abstracts using keywords related to functioning ('Functioning', 'Functional status', 'Function*, 'ICF', 'International Classification of Functioning, Disability and Health', 'Activities of Daily Living' or 'ADL') and casemix systems ('Casemix', 'case mix', 'Diagnosis Related Groups', 'Function Related Groups', 'Resource Utilization Groups' or 'AN-SNAP'). In addition, a hand search of reference lists of included articles was conducted. Information about study aims, design, country, setting, methods, outcome variables, study results, and information regarding the authors' discussion of results, study limitations and implications was extracted.

RESULTS

Ten included studies provided evidence demonstrating that adding functioning information into casemix systems improves predictive ability and fosters homogeneity in casemix groups with regard to costs and length of stay. Collection and integration of functioning information varied across studies. Results suggest that, in particular, DRG casemix systems can be improved in predicting resource use and capturing outcomes for frail elderly or severely functioning-impaired patients.

CONCLUSION

Further exploration of the value of adding functioning information into casemix systems is one promising approach to improve casemix systems ability to adequately capture the differences in patient's needs for services and to better predict resource use.

Australian diagnosis related groups: Drivers of complexity adjustment

BACKGROUND

In undertaking a major revision to the Australian Refined Diagnosis Related Group (ARDRG) classification, we set out to contrast Australia's approach to using data on additional (not principal) diagnoses with major international approaches in splitting base or Adjacent Diagnosis Related Groups (ADRGs).

METHODS

Comparative policy analysis/narrative review of peer-reviewed and grey literature on international approaches to use of additional (secondary) diagnoses in the development of Australian and international DRG systems.

ANALYSIS

European and US approaches to characterise complexity of inpatient care are well-documented, providing useful points of comparison with Australia's. Australia, with good data sources, has continued to refine its national DRG classification using increasingly sophisticated approaches. Hospital funders in Australia and in other systems are often under pressure from provider groups to expand classifications to reflect clinical complexity. DRG development in most healthcare systems reviewed here reflects four critical factors: these socio-political factors, the quality and depth of the coded data available to characterise the mix of cases in a healthcare system, the size of the underlying population, and the intended scope and use of the classification. Australia's relatively small national population has constrained the size of its DRG classifications, and development has been concentrated on inpatient care in public hospitals.

DISCUSSION AND CONCLUSIONS

Development of casemix classifications in health care is driven by both technical and socio-political factors. Use of additional diagnoses to adjust for patient complexity and cost needs to respond to these in each casemix application.

Derivation and validation of the acute organ failure score to predict outcome in critically ill patients: a cohort study

OBJECTIVES

Prediction models for ICU mortality rely heavily on physiologic variables that may not be available in large retrospective studies. An alternative approach when physiologic variables are absent stratifies mortality risk by acute organ failure classification.

DESIGN

Retrospective cohort study.

SETTING

Two large teaching hospitals in Boston, MA.

SUBJECTS

Ninety-two thousand eight hundred eighty-six patients aged 18 years old or older admitted between November 3, 1997, and February 25, 2011, who received critical care.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The derivation cohort consisted of 35,566 patients from Brigham and Women's Hospital, and the validation cohort comprised 57,320 patients from Massachusetts General Hospital. Acute organ failure was determined for each patient based on International Classification of Diseases, 9th Revision, Clinical Modification code combinations. The main outcome measure was 30-day mortality. A clinical prediction model was created based on a logistic regression model describing the risk of 30-day mortality as a function of age, medical versus surgical patient type, Deyo-Charlson index, sepsis, and type acute organ failure (respiratory, renal, hepatic, hematologic, metabolic, and neurologic) after ICU admission. We computed goodness-of fit statistics and c-statistics as measures of model calibration and 30-day mortality discrimination, respectively. Thirty-day mortality occurred in 5,228 of 35,566 patients (14.7%) assigned to the derivation cohort. The clinical prediction model was predictive for 30-day mortality. The c-statistic for the clinical prediction model was 0.7447 (95% CI, 0.74-0.75) in the derivation cohort and 0.7356 (95% CI, 0.73-0.74) in the validation cohort. For both the derivation and validation cohorts, the Hosmer-Lemeshow chi-square p values indicated good model fit. In a smaller cohort of 444 patients with Acute Physiologic and Chronic Health Evaluation II scores determined, differences in model discrimination of 30-day mortality between the clinical prediction model and Acute Physiologic and Chronic Health Evaluation II were not significant (chi-square=0.76; p=0.38).

CONCLUSIONS

An acute organ failure-based clinical prediction model shows good calibration and discrimination for 30-day mortality in the critically ill. The clinical prediction model compares favorably to Acute Physiologic and Chronic Health Evaluation II score in the prediction of 30-day mortality in the critically ill. This score may be useful for severity of illness risk adjustment in observational studies where physiologic data are unavailable.

Introducing diagnosis-related groups: is the information system ready?

Diagnosis-related group (DRG) system is a classification system widely used in health managements, the foundation of which lies in the medical information system. A large effort had been made to improve the quality of discharge data before the introduction of DRGs in Beijing. We extract discharge data from 108 local hospitals spanning 4 years before and after standardization to evaluate the impact of standardization on DRG grouping performance. The data was grouped on an annual basis in accordance with Beijing's local DRG system. Proportion of ungrouped data, coefficient of variation (CV) and reduction in variance (RIV) were used to measure the performance of the DRG system. Both the descriptive and regression analysis indicate a significant reduction in terms of ungrouped data and CV for expenditure, increase of RIV for expenditure and length of stay. However, when there was no intervention, that is, between 2005 and 2006 and between 2008 and 2009, changes in these indicators were all insignificant. Therefore, the standardization of discharge data did improve data quality and consequently enhanced the performance of DRGs. Developing countries with a relatively weak information infrastructure should strengthen their medical information system before the introduction of the DRG system.

Khat chewing and health related quality of life: cross-sectional study in Jazan region, Kingdom of Saudi Arabia

Background

The chewing of Khat leaves, a natural psychoactive substance is widely chewed in countries of East Africa and the southern Arabian Peninsula, and is reported to be associated with a range of unfavorable health outcomes including khat dependence. The impact of Khat chewing on Health Related Quality of Life is yet to be explored. Aims: to measure and compare the quality of life of the khat chewers and non-khat chewers using a short form health survey (SF36), and to assess factors associated with Khat chewing using SF36 in a sample of adult population in Jazan region, Kingdom of Saudi Arabia.

Methods

A total of 630 participants from two independent male populations of khat chewers and non-khat chewers were recruited into a cross-sectional survey study. A self administrative survey based on the SF-36 questionnaire was used to collect data on measures of health-related quality of life (HRQoL). Socioeconomic data of the respondents were also collected for detailed analysis. Data analysis include: descriptive statistics, reliability tests (Cronbach’s alpha and intraclass correlation coefficient), and bivariate analysis (Chi square and Mann–Whitney U-test) to compare HRQoL of Khat chewers and non-Khat chewers.

Results

The odds of being a khat chewer were higher in respondents with a lower socioeconomic status. The SF-36 scores were significantly lower in all domains for respondents with khat chewing, indicating that non-khat chewers had higher health perceptions compared with those chewing khat. The overall mean score of HRQoL for non-khat chewers was 92.7% (SD 5.53) compared with 63.5% (SD 21.73) for the khat chewing group. The study had shown good internal consistency and reproducibility across the eight subscales of SF-36 questionnaire (α 0.74-0.95). The Mann–Whitney U-test showed a significant difference between khat chewers and non-khat chewers (P < 0.001).

Conclusions

This study measured and compared the quality of life of khat chewers and non-khat chewers using a generic health survey (SF36). The study had shown that khat chewing is associated with lower quality of life (HRQoL) and lower socioeconomic status. However in future a more refined SF36 developed especially for Khat chewers can provide more useful information.

Homogeneity of the German diagnosis-related groups

This study analysed the outstanding homogeneity of the German Diagnosis-Related Groups (G-DRG) using the reduction in variance (R²) of costs. Arbitrary increase in case groups, definition of additional charges and combination of several case groups in one DRG were considered as potential confounders. In 2009, the G-DRG-system offers an outstanding homogeneity with R² of 83.5% in comparison to 2004 with R² of 70.2%. The effect of an arbitrary increase in case groups is negligible. However, a simulation of the other confounders explains three-fourth of the increase in R² between 2004 and 2009. The definition of additional charges attributes in particular to the outstanding homogeneity. The assessment of DRG-systems with R² should be complemented with measures that are independent from a trimming of costs, e.g. relating actual costs with prospective payment. The G-DRGs left medical ground in order to achieve optimal economical homogeneity.

Describing Chinese hospital activity with diagnosis related groups (DRGs)

OBJECTIVE

To examine the applicability of an Australian casemix classification system to the description of Chinese hospital activity.

DESIGN

A total of 161,478 inpatient episodes from three Chengdu hospitals with demographic, diagnosis, procedure and billing data for the year 1998/1999, 1999/2000 and 2000/2001 were grouped using the Australian refined-diagnosis related groups (AR-DRGs) (version 4.0) grouper.

MAIN OUTCOME MEASURES

Reduction in variance (R2) and coefficient of variation (CV).

RESULTS

Untrimmed reduction in variance (R2) was 0.12 and 0.17 for length of stay (LOS) and cost respectively. After trimming, R2 values were 0.45 and 0.59 for length of stay and cost respectively.

CONCLUSIONS

The Australian refined DRGs provide a good basis for developing a Chinese grouper.