Allocation Of Care Resources Research Articles

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques. This study aims to test the feasibility of predicting the closure of medical and dental clinics (MCs and DCs, respectively) and investigate important factors associated with their closure using machine running techniques. The units of analysis were MCs and DCs. This study used health insurance administrative data. The participants of this study ran and closed clinics between January 1, 2020, and December 31, 2021. Using all closed clinics, closed and run clinics were selected at a ratio of 1:2 based on the locality of study participants using the propensity matching score of logistic regression. This study used 23 and 19 variables to predict the closure of MCs and DCs, respectively. Key variables were extracted using permutation importance and the sequential feature selection technique. Finally, this study used 5 and 6 variables of MCs and DCs, respectively, for model learning. Furthermore, four machine learning techniques were used: (1) logistic regression, (2) support vector machine, (3) random forest (RF), and (4) Extreme Gradient Boost. This study evaluated the modeling accuracy using the area under curve (AUC) method and presented important factors critically affecting closures. This study used SAS (version 9.4; SAS Institute Inc) and Python (version 3.7.9; Python Software Foundation). The best-fit model for the closure of MCs with cross-validation was the support vector machine (AUC 0.762, 95% CI 0.746-0.777; P<.001) followed by RF (AUC 0.736, 95% CI 0.720-0.752; P<.001). The best-fit model for DCs was Extreme Gradient Boost (AUC 0.700, 95% CI 0.675-0.725; P<.001) followed by RF (AUC 0.687, 95% CI 0.661-0.712; P<.001). The most significant factor associated with the closure of MCs was years of operation, followed by population growth, population, and percentage of medical specialties. In contrast, the main factor affecting the closure of DCs was the number of patients, followed by annual variation in the number of patients, year of operation, and percentage of dental specialists. This study showed that machine running methods are useful tools for predicting the closure of small medical facilities with a moderate level of accuracy. Essential factors affecting medical facility closure also differed between MCs and DCs. Developing good models would prevent unnecessary medical facility closures at the national level.

Forecasting Informal care needs of the urban-rural older adults in China based on microsimulation model

BackgroundForecasting the intensity, source, and cost of informal care for older adults in China is essential to establish and enhance policy support systems for informal care within the context of East Asian traditional culture that emphasizes filial piety. This study aims to analyze the current situation and influencing factors for the informal care needs and predict the trends of informal care needs for older adults in China from 2020 to 2040.MethodsUsing the CHARLS database from 2015 to 2018, this study first combined a two-part model and a multinomial logit to analyze the influencing factors for the informal care needs of urban-rural older adults in China. Secondly, a multi-state Markov model was constructed to forecast the number of urban-rural older populations in each health state from 2020 to 2040. Finally, based on a microsimulation model, this study predicted the trends of informal care intensity, source, and cost for older adults in urban and rural areas from 2020 to 2040.ResultsIn 2040, the size of the disabled older population in China will expand further. In rural areas, the total number of disabled people in 2040 (39.77 million) is 1.50 times higher than that in 2020; In urban areas, the total number of disabled people in 2040 (56.01 million) is 2.51 times higher than that in 2020. Compared with 2020, older adults population with mild, moderate and severe disability in 2040 would increase by 87.60%, 101.70%, and 115.08%, respectively. In 2040, the number of older adults receiving low-, medium-, and high-intensity care in China will be 38.60 million, 22.89 million, and 41.69 million, respectively, and older people will still rely on informal care provided by spouses and children (from spouses only: 39.26 million, from children only: 36.74 million, from spouses and children only: 16.79 million, other: 10.39 million). The total cost of informal care in 2040 will be 1,086.65 billion yuan, 2.22 times that of 2020 (490.31 billion yuan), which grows faster than the economic growth rate.ConclusionFrom 2020 to 2040, the informal care needs of older people in rural areas will increase first and then decrease due to the demographic structure and rapid urbanization. In contrast, the informal care needs of older people in urban areas will continuously increase from 2020 to 2040, with the growth rate gradually slowing down. This study provides an evidence-based rationale for scientifically measuring the economic value of informal care and reasonably allocating care resources.

Trends in the integration of medical corporation hospitals in Japan: a national wide longitudinal study between 2017 and 2021

BackgroundMedical corporations, which are non-profit organizations that aim to operate hospitals, clinics, or long-term care facilities, account for more than half of all hospital beds and play a central role in the health care system in Japan. However, limited analysis of hospital integration has been performed. Examining the hospital integration trends of medical corporations can provide valuable insights for optimizing health care delivery and resource allocation in Japan. This study conducted a comprehensive analysis of trends in the hospital integration of medical corporations and market concentration in Japan using nationwide longitudinal data.MethodsThis longitudinal study evaluated the hospital integration of medical corporations in Japan between 2017 and 2021 using medical corporation financial data provided by the Neostage Company. The target population was medical corporations that own hospitals in Japan. The primary outcomes were the horizontal and vertical integration of medical corporation hospitals. Horizontal integration was defined as the integration of two or more hospitals within the same corporation, while vertical integration was defined as the integration of a hospital with different types of health care facilities, such as clinics or long-term care facilities, within a single medical corporation. The Herfindahl–Hirschman Index (HHI) was calculated to measure hospital market concentration using hospital bed data for medical corporations in all prefectures.ResultsThe number of hospitals and hospital beds within medical corporations decreased from 5,670 to 848,174 in 2017 to 5,486 and 814,462 in 2021, respectively. Both horizontal and vertical integration among medical corporation facilities showed a gradual upward trend, increasing from 24.92% to 55.75% in 2017 to 26.92% and 59.42% in 2021, respectively. The mean (standard deviation [SD]) HHI increased slightly from 262.7 (178.6) in 2017 to 275.2 (187.9) in 2021, with rural areas being more concentrated than urban areas, although both remained unconcentrated.ConclusionsThis longitudinal study revealed a gradual downward trend in the number of hospitals and hospital beds owned by medical corporations, while horizontal and vertical integration gradually progressed without any particular medical corporation dominating the market. These findings are expected to contribute to policymaking efforts aimed at providing optimal health care services in Japan.

Technical efficiency and its determinants in health service delivery of public health centers in East Wollega Zone, Oromia Regional State, Ethiopia: Two-stage data envelope analysis

BackgroundPriority-setting becomes more difficult for decision-makers when the demand for health services and health care resources rises. Despite the fact that the Ethiopian healthcare system places a strong focus on the efficient utilization and allocation of health care resources, studies of efficiency in healthcare facilities have been very limited. Hence, the study aimed to evaluate efficiency and its determinants in public health centers.MethodsA cross-sectional study was conducted in the East Wollega zone, Oromia Regional State, Ethiopia. Ethiopian fiscal year of 2021–2022 data was collected from August 01–30, 2022 and 34 health centers (decision-making units) were included in the analysis. Data envelope analysis was used to analyze the technical efficiency. A Tobit regression model was used to identify determinants of efficiency, declaring the statistical significance level at P < 0.05, using 95% confidence interval.ResultsThe overall efficiency score was estimated to be 0.47 (95% CI = 0.36–0.57). Out of 34 health centers, only 3 (8.82%) of them were technically efficient, with an efficiency score of 1 and 31 (91.2%) were scale-inefficient, with an average score of 0.54. A majority, 30 (88.2%) of inefficient health centers exhibited increasing return scales. The technical efficiency of urban health centers was (β = -0.35, 95% CI: -0.54, -0.07) and affected health centers’ catchment areas by armed conflicts declined (β = -0.21, 95% CI: -0.39, -0.03) by 35% and 21%, respectively. Providing in-service training for healthcare providers increased the efficiency by 27%; 95% CI, β = 0.27(0.05–0.49).ConclusionsOnly one out of ten health centers was technically efficient, indicating that nine out of ten were scale-inefficient and utilized nearly half of the healthcare resources inefficiently, despite the fact that they could potentially reduce their inputs nearly by half while still maintaining the same level of outputs. The location of health centers and armed conflict incidents significantly declined the efficiency scores, whereas in-service training improved the efficiency. Therefore, the government and health sector should work on the efficient utilization of healthcare resources, resolving armed conflicts, organizing training opportunities, and taking into account the locations of the healthcare facilities during resource allocation.

Healthcare utilisation and associated costs for methadone versus buprenorphine recipients: Examination of interlinked primary and secondary care electronic health records in England.

More evidence for patterns of healthcare utilisation and associated costs among people receiving opioid agonist therapy (OAT) is needed. We investigated primary and secondary healthcare usage and costs among methadone and buprenorphine recipients in England. We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases of patients who were prescribed OAT between 1 January 2007 and 31 July 2019. The cohort was linked to Hospital Episode Statistics admitted patient care, outpatient and emergency department data, neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records. Negative binomial regression models were applied to estimate weighted rate ratios (wRR) of healthcare utilisation. Total and mean costs were calculated using Unit Costs of Health and Social Care and the National Healthcare Service Payment by Results National Tariffs. Among 12,639 patients observed over 39,016 person-years, we found higher rate of hospital admissions (wRR 1.18; 1.08-1.28) among methadone compared with buprenorphine recipients. The commonest hospital discharge diagnoses among methadone patients were infectious diseases (19.2%), mental and behavioural disorders (17.0%) and drug-related poisoning (16.5%); the three commonest among buprenorphine patients were mental and behavioural diseases (21.5%), endocrine (13.8%) and genitourinary system diseases (13.1%). Methadone patients had similar mean costs compared with buprenorphine patients (cost difference: £539.01; 432.11-1006.69). Differences in healthcare utilisation frequency for methadone versus buprenorphine recipients were observed. The differences in associated costs were mainly driven by hospital admissions. These findings offer valuable insights for optimising care strategies and resource allocation for OAT recipients.

Predictor Selection for Case Complexity Based on Routine Data for Needs-Based and Competence-Based Staff Planning.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

Modelling climate impacts on paediatric sepsis incidence and severity in Bangladesh.

Sepsis is a leading cause of paediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries. The impacts of climate change on the burden and outcomes of sepsis in low- and middle-income countries, particularly in paediatric populations, remain poorly understood. We aimed to assess the associations between climate variables (temperature and precipitation) and paediatric sepsis incidence and mortality in Bangladesh, one of the countries most affected by climate change. We conducted retrospective analyses of patient-level data from the International Centre for Diarrhoeal Disease Research, Bangladesh, and environmental data from the National Oceanic and Atmospheric Administration. Using random forests, we assessed associations between sepsis incidence and sepsis mortality with temperature and precipitation between 2009-22. A nonlinear relationship between temperature and sepsis incidence and mortality was identified. The lowest incidence occurred at an optimum temperature of 26.6°C with a gradual increase below and a sharp rise above this temperature. Higher precipitation levels showed a general trend of increased sepsis incidence. A similar distribution for sepsis mortality was identified with an optimum temperature of 28°C. Findings suggest that environmental temperature and precipitation play a role in paediatric sepsis incidence and sepsis mortality in Bangladesh. As children are particularly vulnerable to climate impacts, it is important to consider climate change in health care planning and resource allocation, especially in resource-limited settings, to allow for surge capacity planning during warmer and wetter seasons. Further prospective research from more globally representative data sets will provide more robust evidence on the nature of the relationships between climate variables and paediatric sepsis worldwide.

Patterns of disease presentation and outcome during the 2024 Al-Sha’abania mass gathering event in Karbala, Iraq

IntroductionMass gatherings can be defined as spontaneous or planned gatherings of many individuals that lead to an increased burden on local healthcare services. This study was conducted to estimate the prevalence of common disease presentations to temporary healthcare outlets during Al-Sha’abania mass gathering events to improve public health preparedness for future events. MethodA cross-sectional study was conducted during the Al-Sha’abania religious mass gathering in Karbala between February 24 and February 26, 2024, including seven healthcare outlets distributed around Imam Hussein and Imam Abbas Holy Shrines. ResultWe retrieved the records of 6799 patients who attended the temporary healthcare outlets during the data collection period; 3919 (57.6 %) were males. Females (62.0 %) and individuals above the age of 50 (52.7 %) constituted a higher proportion of emergency presentations. The most common cause of attendance was non-communicable conditions in both cold (66.7 %) and emergency cases (49.4 %). However, accidents/injuries were more common in emergency presentations (18.8 % vs. 2.7 %). The highest overall cause of attendance across all categories was joint/bone conditions (1192, 19.26 %) for cold cases and hypertension for emergency cases (84, 13.75 %). Out of the 611 patients with emergency presentation, only 8 (1.3 %) required referral to secondary and tertiary healthcare centers. ConclusionDifferences in the number of cases presented to different healthcare outlets require careful resource allocation during future mass gathering events in Karbala. Additionally, the importance of managing chronic conditions should be emphasized to reduce the burden of non-communicable conditions during mass gathering events.

Challenges and support systems of nurses caring for women with advanced cervical cancer in Accra, Ghana

IntroductionCervical cancer is one of the causes of female deaths worldwide. Cervical cancer incidence is rising with almost three thousand (2797) women in Ghana being diagnosed with the condition each year, with almost two thousand (1,699) of them dying from its complications Nurses caring for cervical cancer patients are exposed to emotional and psychological distress due to late presentation, the burden of care, patients’ suffering and the alarming number of deaths associated with it. Improving positive patient outcomes require identifying the challenges and support systems available to nursing staff so as to harness these support systems for improving care outcomes.AimThis study explored the challenges and support systems of nurses caring for women with advanced cervical cancer in Accra, Ghana.MethodIn this study, we adopted an exploratory qualitative design. The study was conducted among eleven (11) nurses and nine (9) midwives engaged at the national referral hospital in Ghana who were providing care for patients with advanced cervical cancer for over a year who were purposively sampled. The data was collected using in-depth interviews with a pre-tested semi-structure interview guide from the twenty participants. We recorded the interviews using an audio-tape. The audio files were transcribed verbatim and thematic analysis was undertaken with the aid of Nvivo 10.0.ResultsThe challenges when rendering nursing care faced by participants of this study were exposure to frequent deaths, inadequate resources, and workload. Most participants lamented that they received absolutely no support from their workplace, hence their only form of support was from their family and friends. They also added that most of them were general nurses and midwives with no special training in oncology nursing or palliative nursing.ConclusionNurses and midwives experience resource, knowledge and skill challenges when caring for patients with advanced cervical cancer. However, the nurses and midwives had emotional attachment to their jobs and their patients and were not distracted by their bad experiences. We recommend improving resource allocation for cervical cancer care through the National Health Insurance Authority (NHIA), Ghana and increased training of nurses in oncology and palliative nursing by the Ministry of Health, Ghana to improve knowledge and skills of the nurses and midwives caring for women with advanced cervical cancer to improve their quality of care. Further, hospitals must make it a priority to have more nurses and midwives trained in oncology and end of life care to improve the knowledge and skills of nurses and midwives caring for advanced cervical cancer patients. Also, these findings should trigger policy-level discussions at the Ministry of Health, Ghana on the training of specialized nurses and midwives in cancer and end of life care to help Ghana meet the sustainable development goal targets related to health.

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n=1,825,289), UK Biobank (n=502,363), and the UK Household Longitudinal Study (UKHLS) (n=49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR=4.47, hospitalisation IRR=1.84; GP use IRR=2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR=4.52, hospitalisation IRR=1.53, GP use IRR=2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR=2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years. Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

Cross‐sectional evaluation of a clinical decision support tool to identify medication‐related problems at discharge from the acute care setting

AbstractBackgroundThere are many reported pharmacist‐led transitions of care (TOC) programs to address medication‐related problems (MRP) at discharge from the acute care setting. Most have identified time and labor resources as significant limitations. This study aims to assess the effectiveness of a medication risk score (MRS)‐driven clinical decision support system (CDSS) in identifying actionable MRPs and improving medication safety in the acute care discharge TOC setting.MethodsA cross‐sectional analysis was conducted in a cohort of 481 subjects discharged from the acute care setting. The MRS‐CDSS was utilized to identify MRPs and provide recommendations for risk reduction. The distribution of MRPs, recommendations, and their associations with MRS severity were analyzed. Additionally, the potential reduction in MRS per subject and its correlation with MRS severity were examined.ResultsThe median MRS reduction per subject was 2 points, while high/severe‐risk patients showed a median potential reduction of 7 points. Among the identified MRPs (n = 691), drug interaction, drug use without indication, and adverse drug reaction accounted for 89.7% of all MRPs. The top three recommendations, discontinue medication, change the time of administration, and start alternative therapy, represented 94.1% of all recommendations. Stratified analysis by MRS category revealed a significant increase in adverse drug reaction MRPs and recommendations to discontinue medications with higher MRS severity. The results were consistent with previous outpatient studies, supporting the MRS‐CDSS's ability to enhance medication safety.ConclusionThis study demonstrates that the MRS‐CDSS effectively identifies actionable MRPs and has the potential to substantially reduce overall pharmacotherapy regimen risk when applied during acute care discharge TOC. The findings support implementable recommendations directed at patient safety and the allocation of health care resources to high‐risk patients for maximum benefit.

Cost-effectiveness of a multicomponent-adherence intervention in fracture liaison services

ABSTRACT Background This study aims to assess the lifetime cost-effectiveness of a multi-component adherence intervention (MCAI), including a patient decision aid and motivational interviewing, compared to usual care in patients with a recent fracture attending fracture liaison services (FLS) and eligible for anti-osteoporosis medication (AOM). Research design and methods Data on AOM initiation and one-year persistence were collected from a quasi-experimental study conducted between 2019 and 2023 in two Dutch FLS centers. An individual level, state-transition Markov model was used to simulate lifetime costs and quality-adjusted life years (QALYs) with a societal perspective of MCAI vs usual care. One-way and probabilistic sensitivity analyses were conducted including variation in additional FLS and MCAI costs (no MCAI cost in baseline). Results MCAI was associated with gain in QALYs (0.0012) and reduction in costs (-€16) and is therefore dominant. At the Dutch willingness-to-pay threshold of €50,000/QALY, MCAI remained cost-effective when increasing costs of the FLS visit or the yearly maintenance cost for MCAI up to +€60. Probabilistic sensitivity analysis demonstrated MCAI to be dominant in 54% of the simulations and cost-effective in 87% with a threshold of €50,000/QALY. Conclusions A MCAI implemented in FLS centers may lead to cost-effective allocation of resources in FLS care, depending on extra costs.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BackgroundDiagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.MethodsThis cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.ResultsUndercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.ConclusionIn Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Addressing Hospital Overwhelm During the COVID-19 Pandemic by Using a Primary Health Care-Based Integrated Health System: Modeling Study.

After strict COVID-19-related restrictions were lifted, health systems globally were overwhelmed. Much has been discussed about how health systems could better prepare for future pandemics; however, primary health care (PHC) has been largely ignored. We aimed to investigate what combined policies PHC could apply to strengthen the health care system via a bottom-up approach, so as to better respond to a public health emergency. We developed a system dynamics model to replicate Shanghai's response when COVID-19-related restrictions were lifted. We then simulated an alternative PHC-based integrated health system and tested the following three interventions: first contact in PHC with telemedicine services, recommendation to secondary care, and return to PHC for recovery. The simulation results showed that each selected intervention could alleviate hospital overwhelm. Increasing the rate of first contact in PHC with telemedicine increased hospital bed availability by 6% to 12% and reduced the cumulative number of deaths by 35%. More precise recommendations had a limited impact on hospital overwhelm (<1%), but the simulation results showed that underrecommendation (rate: 80%) would result in a 19% increase in cumulative deaths. Increasing the rate of return to PHC from 5% to 20% improved hospital bed availability by 6% to 16% and reduced the cumulative number of deaths by 46%. Moreover, combining all 3 interventions had a multiplier effect; bed availability increased by 683%, and the cumulative number of deaths dropped by 75%. Rather than focusing on the allocation of medical resources in secondary care, we determined that an optimal PHC-based integrated strategy would be to have a 60% rate of first contact in PHC, a 110% recommendation rate, and a 20% rate of return to PHC. This could increase health system resilience during public health emergencies.

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD. The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total). The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = - 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811). Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.

Workforce thresholds and the non-linear association between registered nurse staffing and care quality in long-term residential care: A retrospective longitudinal study of English care homes with nursing

BackgroundCare needs amongst 425,000 dependent older residents in English care homes are becoming more complex. The quality of care in these homes is influenced by staffing levels, especially the presence of registered nurses (RNs). Existing research on this topic, often US-focused and relying on linear assumptions, has limitations. This study aims to investigate the non-linear relationship between RN staffing and care quality in English care homes using machine learning and administrative data from two major care home providers. MethodsA retrospective observational study was conducted using data from two English care home providers. Each was analysed separately due to variations in data reporting and care processes. Various care quality indicators and staffing metrics were collected for a 3.5-year period. Regression analysis and machine learning (random forest) were employed to identify non-linear relationships. Ethical approval was obtained for the study. ResultsUsing linear methods, higher skill mix – more care provided by RNs – was associated with lower incidence of adverse outcomes, such as urinary tract infections and hospitalisations. However, non-linear skill mix–outcome relationship modelling revealed both low and high skill mix levels were linked to higher risks. The effects of agency RN usage varied between providers, increasing risks in one but not the other. DiscussionThe study highlights the cost implications of increasing RN staffing establishments to improve care quality, suggesting a non-linear relationship and an optimal staffing threshold of around one-quarter of care provided by nurses. Alternative roles, such as care practitioners, merit exploration for meeting care demands whilst maintaining quality. This research underscores the need for a workforce plan for social care in England. It advocates for the incorporation of machine learning models alongside traditional regression-based methods. Our results may have limited generalisability to smaller providers and experimental research to redesign care processes effectively may be needed. ConclusionRNs are crucial for quality in care homes. Contrary to the assumption that higher nurse staffing necessarily leads to better care quality, this study reveals a nuanced, non-linear relationship between RN staffing and care quality in English care homes. It suggests that identifying an optimal staffing threshold, beyond which increasing nursing inputs may not significantly enhance care quality may necessitate reconsidering care system design and (human) resource allocation. Further experimental research is required to elucidate resource-specific thresholds and further strengthen evidence for care home staffing. Tweetable abstractHow much nursing care is needed to assure quality in care homes? Evidence from 2 English care home providers shows that nurse sensitive outcomes (an indicator of quality) are better when ~25 % of care is provided by nurses. Nurse shortages increase risks for residents.

Comparison of Sensitivity, Specificity, and Accuracy of ISS, RTS, and TRISS Instruments in Predicting Mortality of Trauma Patients

This study aims to compare the effectiveness of three trauma scoring systems—Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS)—in predicting mortality among trauma patients at Inche Abdoel Moeis Regional General Hospital Samarinda's emergency room. Trauma assessment tools are crucial for reducing mortality rates and enhancing patient outcomes. While ISS focuses on anatomical factors, RTS integrates physiological parameters, and TRISS combines both for a comprehensive evaluation. Previous research suggests their effectiveness in mortality prediction, albeit with variations in performance. The absence of a dedicated trauma assessment system at the hospital underscores the urgency of this research. By evaluating the performance of ISS, RTS, and TRISS, this study aims to inform clinical practice and enhance trauma patient management, ultimately reducing mortality rates and improving outcomes in the emergency room setting. The study's methodology involves retrospective analysis of trauma patient data, including demographics, injury characteristics, and outcomes. Statistical analysis will compare the performance of the three scoring systems in predicting mortality with implications for trauma care protocols and resource allocation. Results will contribute to evidence-based decision-making in emergency room settings, facilitating more accurate triage and treatment strategies for trauma patients.

Care trajectories around a first dementia diagnosis in patients with serious mental illness.

To develop a typology of care trajectories (CTs) 1 year before and after a first dementia diagnosis in individuals aged ≥65 years, with prevalent schizophrenia or bipolar disorder. This was a longitudinal, retrospective cohort study using health administrative data (1996-2016) from Quebec (Canada). We selected patients aged ≥65 years with an incident diagnosis of dementia between 1 January 2014 and 31 December 2016, and a diagnosis of schizophrenia and/or or bipolar disorder. A CT typology was generated by a multidimensional state sequence analysis based on the "6 W" model of CTs. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why") and the specialty of care providers ("which"). In total, 3868 patients were categorized into seven distinct types of CTs, with varying patterns of healthcare use and comorbidities. Healthcare use differed in terms of intensity, but also in its distribution around the diagnosis. For instance, whereas one group showed low healthcare use, healthcare use abruptly increased or decreased after the diagnosis in other groups, or was equally distributed. Other significant differences between CTs included mortality rates and use of long-term care after the diagnosis. Most patients (67%) received their first dementia diagnosis during hospitalization. Our innovative approach provides a unique insight into the complex healthcare patterns of people living with serious mental illness and dementia, and provides an avenue to support data-driven decision-making by highlighting fragility areas in allocating care resources. Geriatr Gerontol Int 2024; 24: 577-586.

독일 연방헌법재판소 ‘Triage’ 결정(2021) 분석을 통해 본 장애인에 대한 차별 금지

During the COVID-19 pandemic, there were concerns that the German healthcare system's triage system for prioritizing care could be in crisis. ‘Triage’ is the process by which healthcare providers decide which patients are prioritized for treatment in situations where critical care medical resources cannot keep up with the demand. There are understandable concerns that people with disabilities, who are at higher risk of becoming critically ill with the COVID-19 virus, could be unfavorably treated by the way these decisions are made. The applicants argued that federal and state measures to prevent this were inadequate and insufficient in the face of the uncertainty of a possible shortage of medical resources. The legislator was therefore obliged to provide a legal basis for triage and to effectively regulate the classification to prevent discrimination. The Federal Constitutional Court found that the classification of patients during the COVID-19 pandemic exposed the applicants to the risk of being disadvantaged on account of their disability in relation to the allocation of intensive care resources. The Court also ordered the legislator to fulfill its duty to act promptly and to take preventive measures to prevent discrimination against persons with disabilities. This decision has been criticized of overlooking the argument that the consideration of the severity of the disease and the course of the disease in the triage process constitutes indirect discrimination against people with disabilities. The Federal Constitutional Court also left open the possibility of indirect discrimination when it did not declare unconstitutional the criteria for determining the likelihood of clinical success, i.e., the likelihood of surviving an acute illness. The ‘triage criterion’ may also violate the European Convention on Human Rights because the European Court of Human Rights has recognized a comprehensive concept of indirect discrimination within the Convention. In this case, the Triage Criteria could put people with disabilities at a disadvantage compared to people without disabilities in that the criteria of disability could still have a negative impact on the course and severity of COVID-19-19 infection, which constitutes indirect discrimination. The Federal Constitutional Court should endeavor to decide important and urgent issues promptly, but not to lose sight of the underlying approach to the problem and the various forms of discrimination that can result from it.

Validation of the Barthel Index in Chinese nursing home residents: an item response theory analysis.

The Barthel Index (BI) is used to standardize the grading of assessments for clinical needs, insurance support, and long-term care resource allocation in China. However, its psychometric properties among nursing home residents remain unclear. Therefore, this study aims to assess and modify the psychometric properties of BI in nursing home residents. A total of 1,402 individuals undergoing evaluation in a nursing home facility in China were included in this study from November 2021 to November 2022. Correlations between items were examined to identify the potential multicollinearity concerns. The unidimensional item response theory (IRT) was used to validate and modify the single structure of BI. Furthermore, the logistic regression/IRT hybrid DIF detection method was conducted to assess differential item functioning (DIF) between the dementia group and the normal group. The pairing of items 5 ("bowl control") and 6 ("bladder control") revealed a local dependence issue, leading to their consolidation. Items 56 (bowel and bladder control) and 9 (mobility) both displayed poor fit indices and underwent category collapsing. Through the application of the generalized partial credit model, the adjusted scale displayed better fit indices, demonstrating a robust discriminative power (DC >1.5) and orderly thresholds. Furthermore, non-uniform DIF was identified in item 2 (bathing) between the dementia group and the normal group. The modified BI demonstrated favorable psychometric properties and proved to be suitable for evaluating nursing home residents experiencing moderate functional impairment, which may provide a precise evaluation for long-term care resource allocation. Future studies could explore integrating supplementary measurements, such as objective indices, to assess a broader spectrum of functional statuses to potentially enhance the limited precision width observed in BI.