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

Impact of the COVID-19 Pandemic on the Incidence of Suicidal Behaviors: A Retrospective Analysis of Integrated Electronic Health Records in a Population of 7.5 Million

The COVID-19 pandemic has caused remarkable psychological overwhelming and an increase in stressors that may trigger suicidal behaviors. However, its impact on the rate of suicidal behaviors has been poorly reported. We conducted a population-based retrospective analysis of all suicidal behaviors attended in healthcare centers of Catalonia (northeast Spain; 7.5 million inhabitants) between January 2017 and June 2022 (secondary use of data routinely reported to central suicide and diagnosis registries). We retrieved data from this period, including an assessment of suicide risk and individuals' socioeconomic as well as clinical characteristics. Data were summarized yearly and for the periods before and after the onset of the COVID-19 pandemic in Spain in March 2020. The analysis included 26,458 episodes of suicidal behavior (21,920 individuals); of these, 16,414 (62.0%) were suicide attempts. The monthly moving average ranged between 300 and 400 episodes until July 2020, and progressively increased to over 600 episodes monthly. In the postpandemic period, suicidal ideation increased at the expense of suicidal attempts. Cases showed a lower suicide risk; the percentage of females and younger individuals increased, whereas the prevalence of classical risk factors, such as living alone, lacking a family network, and a history of psychiatric diagnosis, decreased. In summary, suicidal behaviors have increased during the COVID-19 pandemic, with more episodes of suicidal ideation without attempts in addition to younger and lower risk profiles

Therapist perceptions of the implementation of a new screening procedure using the ItFits-toolkit in an iCBT routine care clinic: A mixed-methods study using the consolidated framework for implementation research

IntroductionThis study investigates the implementation of a new, more automated screening procedure using the ItFits-toolkit in the online clinic, Internet Psychiatry (iPsych) (www.internetpsykiatrien.dk), delivering guided iCBT for mild to moderate anxiety and depressive disorders. The study focuses on how the therapists experienced the process.MethodsQualitative data were collected from semi-structured individual interviews with seven therapists from iPsych. The interviews were conducted using an interview guide with questions based on the Consolidated Framework for Implementation Research (CFIR). Quantitative data on the perceived level of normalization were collected from iPsych therapists, administrative staff, and off-site professionals in contact with the target demographic at 10-time points throughout the implementation.ResultsThe therapists experienced an improvement in the intake procedure. They reported having more relevant information about the patients to be used during the assessment and the treatment; they liked the new design better; there was a better alignment of expectations between patients and therapists; the patient group was generally a better fit for treatment after implementation; and more of the assessed patients were included in the program. The quantitative data support the interview data and describe a process of normalization that increases over time.DiscussionThe ItFits-toolkit appears to have been an effective mediator of the implementation process. The therapists were aided in the process of change, resulting in an enhanced ability to target the patients who can benefit from the treatment program, less expenditure of time on the wrong population, and more satisfied therapists

Toward Sustainable Adoption of Integrated Care for Prevention of Unplanned Hospitalizations: A Qualitative Analysis

Introduction: Complex chronic patients are prone to unplanned hospitalizations leading to a high burden on healthcare systems. To date, interventions to prevent unplanned admissions show inconclusive results. We report a qualitative analysis performed into the EU initiative JADECARE (2020-2023) to design a digitally enabled integrated care program aiming at preventing unplanned hospitalizations. Methods: A two-phase process with four design thinking (DT) sessions was conducted to analyse the management of complex chronic patients in the region of Catalonia (ES). In Phase I, Discovery, two DT sessions, October 2021 and February 2022, were done using as background information: i) the results of twenty structured interviews (five patients and fifteen professionals), ii) two governmental documents on regional deployment of integrated care and on the Catalan digital health strategy, respectively, and iii) the results of a cluster analysis of 761 hospitalizations. In Phase II, Confirmation, we examined the 30- and 90 -day post -discharge periods of 49,604 hospitalizations as input for two additional DT sessions conducted in November and December 2022. Discussion: The qualitative analysis identified poor personalization of the interventions, the need for organizational changes, immature digitalization, and suboptimal services evaluation as main explanatory factors of the observed efficacyeffectiveness gap. Additionally, a program for prevention of unplanned hospitalizations, to be evaluated during the period 2024-2025, was generated. Conclusions: A digitally enabled adaptive case management approach to foster collaborative work and personalization of care, as well as organizational re -engineering, are endorsed for value -based prevention of unplanned hospitalizations

The adjusted morbidity groups (GMA): an exhaustive and severity-balanced tool for risk assessment

Grups morbiditat ajustada; GMA; Eina d'estratificació; Avaluació de riscosGrupos morbilidad ajustada; GMA; Herramienta de estratificación; Evaluación de riesgosAdjusted morbidity groups; GMA; Stratification tool; Risk assessmentEls GMA consisteixen en una eina que permet avaluar el risc en salut a partir de les característiques demogràfiques dels pacients, les seves malalties cròniques i aquelles situacions o malalties agudes que puguin tenir-hi impacte. Aquesta eina proporciona un índex de risc que es pot utilitzar com a factor d’ajust en models específics d’una determinada malaltia i a la vegada actua com un agrupament per estratificar la població en diferents nivells de risc.Los GMA consisten en una herramienta que permite evaluar el riesgo en salud a partir de las características demográficas de los pacientes, sus enfermedades crónicas y aquellas situaciones o enfermedades agudas que puedan tener impacto. Esta herramienta proporciona un índice de riesgo que se puede utilizar como factor de ajuste en modelos específicos de una determinada enfermedad y al mismo tiempo actúa como un agrupamiento para estratificar la población en diferentes niveles de riesgo.GMAs are a tool that assesses health risk based on the demographic characteristics of patients, their chronic diseases and those situations or acute diseases that may have an impact. This tool provides a risk index that can be used as an adjustment factor in specific models of a given disease and at the same time acts as a grouping to stratify the population at different levels of risk

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

Background: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. Methods: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation

Effectiveness of an integrated care program for intensive home care services after discharge of stroke patients

The continuity of care in hospital discharge is a cornerstone of patient-centred care, particularly after an acute episode with a high impact on patients’ autonomy. In the setting of stroke, a highly disabling disease, early delivery of post-discharge support services has been associated with better health outcomes. However, the lack of integration between social and health care services often delays the start of home care services in these patients, likely worsening health outcomes. In our area, a post-stroke intensive home care program (RHP) was launched to integrate social and health care services for improving the domiciliary care of stroke patients after hospital discharge

In the era of remote access to the healthcare system, the accurate measures of patient’s health complexity is a mainstay for a person-centred service allocation

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Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates