Diagnostic Related Groups Research Articles

A novel, integrated approach for understanding and investigating Healthcare Associated Infections: A risk factors constellation analysis.

Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clusters within healthcare settings; evaluation of attributable incidence, mortality, and disability-adjusted life years (DALYs); and implementation of governance policies. In Italy, the burden of HAIs is estimated to be 702.53 DALYs per 100,000 population, which has the same magnitude as the burden of ischemic heart disease. However, data in EHRs are usually not homogeneous, not properly linked and engineered, or not easily compared with other data. Moreover, without a proper epidemiological approach, the relevant information may not be detected. In this retrospective observational study, we established and engineered a new management system on the basis of the integration of microbiology laboratory data from the university hospital "Policlinico Tor Vergata" (PTV) in Italy with hospital discharge forms (HDFs) and clinical record data. All data are currently available in separate EHRs. We propose an original approach for monitoring alert microorganisms and for consequently estimating HAIs for the entire period of 2018. Data extraction was performed by analyzing HDFs in the databases of the Hospital Information System. Data were compiled using the AREAS-ADT information system and ICD-9-CM codes. Quantitative and qualitative variables and diagnostic-related groups were produced by processing the resulting integrated databases. The results of research requests for HAI microorganisms and AMR profiles sent by the departments of PTV from 01/01/2018 to 31/12/2018 and the date of collection were extracted from the database of the Complex Operational Unit of Microbiology and then integrated. We were able to provide a complete and richly detailed profile of the estimated HAIs and to correlate them with the information contained in the HDFs and those available from the microbiology laboratory. We also identified the infection profile of the investigated hospital and estimated the distribution of coinfections by two or more microorganisms of concern. Our data were consistent with those in the literature, particularly the increase in mortality, length of stay, and risk of death associated with infections with Staphylococcus spp, Pseudomonas aeruginosa, Klebsiella pneumoniae, Clostridioides difficile, Candida spp., and Acinetobacter baumannii. Even though less than 10% of the detected HAIs showed at least one infection caused by an antimicrobial resistant bacterium, the contribution of AMR to the overall risk of increased mortality was extremely high. The increasing availability of health data stored in EHRs represents a unique opportunity for the accurate identification of any factor that contributes to the diffusion of HAIs and AMR and for the prompt implementation of effective corrective measures. That said, artificial intelligence might be the future of health data analysis because it may allow for the early identification of patients who are more exposed to the risk of HAIs and for a more efficient monitoring of HAI sources and outbreaks. However, challenges concerning codification, integration, and standardization of health data recording and analysis still need to be addressed.

Challenges of implementing diagnostic-related groups and healthcare promotion in Iran: A strategic applied research.

Implementing the diagnostic-related groups (DRGs) promotes the efficiency of healthcare. Therefore, the present study aimed to identify the challenges facing implementing the DRGs in Iran. The present study is a strategic applied research conducted in two phases. In the first phase, the challenges facing DRGs were extracted through a literature review. Then the collected data is entered into a checklist consisting of five sections including technological, cultural, organizational, strategic, and natural challenges. In the second phase, data were collected by purposive sampling and semistructured interviews with 10managers of the Medical Services Organization of Tehran, Iran. Data analysis was performed by conventional content analysis using MAXQDA software and descriptive using SPSS software version 19. The challenges facing the implementing DGRs from the experts'perspective included technological, organizational, nature, strategic, and cultural in order of priority. The three main fundamental challenges were reported; lack of integrating the DGRs with health information system(70%),frequent changes of management (70%),reducing the quality of care following early patient discharge (60%). The results of the present study showed that the DRG system faced with challenges and healthcare officials should apply policies and guidelines to reform the system before changing the reimbursement system in Iran. By considering the leading countries experiences in the nationalizing the DRG system field, the problems and solutions of the system can be identified and aid in the more successful implementation of these systems.

Κοστολόγηση Υπηρεσιών στη Μονάδα Εντατικής Θεραπείας με τη χρήση των DRGs στις χώρες του ΟΟΣΑ

The trend for quality, efficient, and productive financing of health service providers, especially Intensive Care Units (ICUs), has become increasingly evident in recent decades. In this study, we investigate the prospective reimburse- ment method, per case of hospitalization in the ICU, according to the Diagnostic Related Groups (DRGs), in the OECD countries, as well as the appropriateness of using the method, in the particular environment of the ICU. A systematic review of papers published up until July 2022 was conducted. The search was performed on PubMed, Medline, Scopus, ScienceDirect, and Google Scholar. The criteria for inclusion in this review were the calculation by researchers of the appropriateness of using DRGs in the ICU, as a method of reimbursement, through comparison with the actual cost of hospitalization of patients in the ICU. Only sixteen(16) of the scientific papers retrieved met the criteria for inclusion in this review. Ten(10) studies assessed the appropriateness of applying DRGs as a reim- bursement method in adult ICUs and six(6) assessed the appropriateness of applying DRGs as a reimbursement method in Neonatal Intensive Care Units and pediatric ICUs. Discrepancies were observed between the actual cost of hospitalization and the reimbursement amount in all categories of ICUs. Finding the right DRG system, combined with other financing methods, would be a useful policy tool to help manage finances, contain costs, and provide quality health services.

Understanding Common Rationales Adopted in Healthcare Price Setting Across the Private Healthcare Sector in Sri Lanka: A qualitative study

Background / Objectives: The Sri Lankan healthcare system consists of public and private sectors. In terms of capacity, the public sector dominates the provision of care across curative, preventive and outpatient care. The private sector, too, has grown rapidly in recent years but was mainly confined to providing curative, diagnostic and outpatient care. Since, there are little or no studies conducted in Sri Lanka thus far, the objective of this study was to understand the current approaches adopted in determining the base of healthcare payments. This study also investigated the economic and administrative processes involved in determining the level of healthcare pricing in the private sector healthcare industry in Sri Lanka. Method: This qualitative study investigated the rationales adopted in healthcare pricing by healthcare administrators in the private sector. Structured interviews were conducted and thematic analysis were applied to interview data collected and analysis. Results: Five key themes, which influenced pricing, were identified from the interviews. These themes included influence from medical practitioners/clinicians, competitor pricing, price adjustment/profit margins, consumables and staff, and economic demands. There was a consensus that competitor pricing and seniority of the practicing clinicians had an impact on pricing. Conclusion: This study revealed that the base of payment in private sector healthcare is fee for service (FFS). Adopting popular international approaches such as Diagnostic Related Groups (DRGS) was not shown in this study. Further, it was evident that the Sri Lankan private healthcare sector administrators unilaterally fix pricing based on the identified key themes without adequately consulting the healthcare payers and users.

Recording adverse events following orthopaedic trauma: Financial implications and validation of an adverse event assessment form in an Irish regional trauma unit

BackgroundIn Ireland, funding of orthopaedic trauma is based on an activity-based funding (ABF) model. Clinically similar cases are split into diagnostic-related groups (DRG), with base funding per DRG provided. Increased complexity of cases (length of stay; complications incurred; occurrence of adverse events) attracts additional remuneration to the base funding. In our institution these adverse events are recorded via retrospective chart-abstraction methods by administrative staff. Incidences which are not included from this review affect both follow up with family physicians and patient care; as well as skewing budgetary decisions that impact fiscal viability of the service. The aim of this study was to compare a prospectively implemented adverse events form with the current national retrospective chart abstraction method. Our outcomes in terms of pay-by-results financial implications. MethodsAn adverse events database adapted from a similar validated model was used to prospectively record complications in 216 patients admitted via the orthopaedic trauma service. Data was contemporaneously collected via a GDPR compliant secure medical messaging platform. Results were compared with the same cohort using an existing data abstraction method. Both data sets were coded in accordance with current standards for case funding. ResultsOverall, 49 adverse events were recorded during the study through prospective charting of adverse events, compared with 26 events documented by customary method (p<0.01).Anaemia requiring blood transfusion n = 11 22.4%) was the most common complication, followed by delirium n = 6 (12%), acute kidney injury n = 6 (12%), and pneumonia n = 5 (10.2%). Missed appropriate funding through conventional methods totalled €40,293 . ConclusionThis pilot study demonstrates the ability to improve capture of adverse events through use of a well-designed assessment form. Proper perioperative data handling is a critical aspect of financial subsidies, enabling optimal allocation of funds.

Surgical procedure long terms recognition from Chinese literature incorporating structural feature

With rapid development of technologies in medical diagnosis and treatment, the novel and complicated concepts and usages of clinical terms especially of surgical procedures have become common in daily routine. Expected to be performed in an operating room and accompanied by an incision based on expert discretion, surgical procedures imply clinical understanding of diagnosis, examination, testing, equipment, drugs and symptoms, etc., but terms expressing surgical procedures are difficult to recognize since the terms are highly distinctive due to long morphological length and complex linguistics phenomena. To achieve higher recognition performance and overcome the challenge of the absence of natural delimiters in Chinese sentences, we propose a Named Entity Recognition (NER) model named Structural-SoftLexicon-Bi-LSTM-CRF (SSBC) empowered by pre-trained model BERT. In particular, we pre-trained a lexicon embedding over large-scale medical corpus to better leverage domain-specific structural knowledge. With input additionally augmented by BERT, rich multigranular information and structural term information is transferred from Structural-SoftLexicon to downstream model Bi-LSTM-CRF. Therefore, we could get a global optimal prediction of input sequence. We evaluate our model on a self-built corpus and results show that SSBC with pre-trained model outperforms other state-of-the-art benchmarks, surpassing at most 3.77% in F1 score. This study hopefully would benefit Diagnostic Related Groups (DRGs) and Diagnosis Intervention Package (DIP) grouping system, medical records statistics and analysis, Medicare payment system, etc.

Are Trends in Revision Total Joint Arthroplasty Sustainable? Declining Inflation-Adjusted Medicare Reimbursement for Hospitalizations

BackgroundWhile the burden of revision total joint arthroplasty (TJA) procedures increases within the United States, it is unclear whether health care resource allocation for these complex cases has kept pace. This study examined the trends in hospital-level reimbursements for revision TJA hospitalizations. MethodsThe Centers for Medicare and Medicaid Services (CMS) inpatient utilization and payment public use files from 2014 to 2019 were queried for diagnostic-related groups (DRGs) for revision TJA: DRG 467 (revision of hip or knee arthroplasty with complication or comorbidity [CC]) and DRG 468 (revision of hip or knee arthroplasty without CC or major CC). From 2014 to 2019, 170,808 revision TJA hospitalizations were billed to Medicare, and revision TJA procedures increased by 3,121 (10.7%). After adjusting to 2019 US dollars with the consumer price index, a multiple linear mixed-model regression analysis was performed. Analysis of covariance compared regressions from 2014 to 2019 for mean-adjusted Medicare payment and mean- adjusted charge were submitted for these DRGs. ResultsMean-adjusted average Medicare payment for DRG 467 decreased by $804.37 (−3.5%) from 2014 to 2019, whereas, that for DRG 468 decreased by $647.33 (−3.6%). The average inflation-adjusted Medicare payment for DRG 467 decreased at a greater rate during the study period, compared to that for DRG 468 (P = .02). ConclusionThe decline in reimbursement for DRGs 467 and 468 reveals decreasing incentives for revision TJA hospitalizations. Further research should assess the efficacy of current Medicare payment algorithms and identify modifications which may provide for fair hospital level reimbursements.

Healthcare cost expenditures associated to frailty and sarcopenia

ObjectivesFrailty and sarcopenia have been related with adverse events, including hospitalization. However, its combined effect with hospitalization-related outcomes, including costs, has not been previously investigated. Our purpose was to explore how frailty, sarcopenia and its interaction could impact on healthcare expenditures.Methods1358 community-dwelling older adults from the Toledo Study of Healthy Ageing (TSHA) were included. Sarcopenia was measured using the Foundation for the National Institutes of Health criteria fitted to our cohort. Frailty was defined according to Frailty Trait Scale 5 (FTS5) and the Frailty Index fitted to the cut-off points of TSHA population. Hospitalization costs were taken from hospital records and costs were attributed according to Diagnostic-Related Groups, using as the cost base year 2015. Two-part regression models were used to analyze the relationship between frailty and sarcopenia and hospital admission, number of hospitalizations, length of stay and hospitalization costs.ResultsSarcopenia was associated only with the probability of being admitted to hospital. Frailty was also associated with higher hospital use, regardless of the frailty tool used, but in addition increased hospital admission costs at follow-up by 23.72% per year and by 19.73% in the full model compared with non-frail individuals. The presence of sarcopenia did not increase the costs of frailty but, by opposite, frailty significantly increased the costs in people with sarcopenia, reaching by 46–56%/patient/year at follow-up. Older adults with frailty and sarcopenia had a higher risk of hospitalization, disregarding the tool used to assess frailty, and higher hospitalization costs (FTS5) in the full model, at the cross-sectional and at the follow-up level.ConclusionsFrailty is associated with increased hospitalization costs and accounts for the potential effects of sarcopenia.

Impact of a standardised rapid response system on clinical outcomes of female patients: an interrupted time series approach

BackgroundThis study aimed to assess the impact of a standardised rapid response systems (the Between the Flags (BTF)) implemented across New South Wales (NSW), Australia, among female patients.MethodsWe conducted an...

Profit-Influencing Factors in Orthopedic Surgery: An Analysis of Costs and Reimbursements.

The aging population and the associated demand for orthopedic surgeries are increasing health costs. Although the Diagnostic Related Groups (DRG) system was introduced to offer incentives for hospitals, concerns remain that reimbursements for older and frail patients do not cover all hospital expenses. We investigated further: (1) Does age influence net financial results in orthopedic surgery? (2) Are there patient or surgical factors that influence results? This retrospective, monocentric study compares costs and reimbursements for orthopedic patients in a tertiary care hospital in Switzerland between 2015 and 2017. The data of 1230 patients were analyzed. Overall, the net results for the hospital were positive, despite 19.5% of patients being treated at a loss. We did not find any correlation between age and profitability (p = 0.61). Patient-related factors associated with financial losses were female sex (p < 0.001) and diabetes (p = 0.013). Patients free of serious comorbidities (p = 0.012) or with a higher cost weight (p < 0.001) were more often profitable. A longer length of stay was associated with higher losses (p < 0.001). This is the first study to address the Swiss DRG reimbursement system in a broad orthopedic population, while also analyzing specific patient and surgical factors. Overall, the reimbursement system is fair, but could better account for certain interventions.

Проблеми термінології та клінічного кодування некротизуючого фасціїту

Purpose - to analyze the typical defects of the formulation of the diagnosis of necrotizing fasciitis (NF), to verify the lexically correct term NF, to develop proposals for the correct coding of NF according to ICD-10-AM. Materials and methods. The medical records of 150 patients who during 1999-2021 were treated in two hospitals of Lviv City (Ukraine) for necrotizing fasciitis was analyzed. Compared diagnoses when referred to the hospital, preliminary and final clinical diagnoses, preoperative and postoperative diagnoses, recorded their statistical codes for ICD-10. An analysis of Ukrainian and Russian-language surgical publications available in Ukraine for the period from 1985 to 2021 on the keywords «surgical soft tissue infection», «necrotizing fasciitis», «soft tissue necrosis». Results. In 142 (95.0%) of the 150 patients operated on for NF, the diagnosis was not correctly formulated during referral. Most often NF was directed and in the initial stages was treated under the guise of other diagnoses: «phlegmon», «erysipelas», «thrombophlebitis», «gangrene», «perianal abscess». NF was suspected by the surgeon prior to the first surgery only in 53 (35.33%) patients. In other cases, it was diagnosed intraoperatively or during repeated interventions. Codes corresponding to other forms of surgical infection were often used for statistical coding of NF. We also worked on lexical variants of the term «necrotizing fasciitis» in Ukrainian language. Conclusions. It was worked out the most correct terminologically Ukrainian equivalent of the diagnosis «necrotizing fasciitis». For statistical coding of NF, it should be designated as the main active disease by code M72.6. The use of a unified term and statistical coding will allow in the future to form a single within the state diagnostic-related group for fair reimbursement of funds to health care providers for treated cases of NF. The research was carried out in accordance with the principles of the Helsinki declaration. The study protocol was approved by the Local ethics committee of all participating institutions. The informed consent of the patient was obtained for conducting the studies. No conflict of interests was declared by the authors. Key words: necrotizing fasciitis, terminology, international classification of diseases.

Clinical and Economic Impact of COVID-19 in Vascular Surgery at a Tertiary University “Hub” Hospital of Italy

BackgroundSince the first cases of a novel respiratory disease were reported in December 2019, coronavirus disease (COVID-19) Emergency State (Cov-ES) caused a worldwide outbreak requiring a complete reorganization of the healthcare system and new management of its personnel; aim of this study was to analyze the clinical and financial impact of Cov-ES in the Department of Vascular Surgery at a Tertiary University “Hub” Hospital in northeast Italy.MethodsDifferences in clinical practice according to Diagnostic Related Group (DRG) and International Statistical Classification of Diseases (ICD) and Related Health Problems and the financial impact of Cov-ES were considered. Vascular procedures performed between March 2019 and December 2019 (Prepandemic) were compared to those performed in the period March–December 2020 (Pandemic). Prepandemic and pandemic reimbursements of all vascular activities and the top 3 vascular diagnoses were evaluated.ResultsPrepandemic versus pandemic era documented a decrease of vascular consultations performed (2,882 vs. 2,270, −21.2%). The number of total vascular procedures decreased from 997 to 797 (−20.1%) with a higher reduction observed in outpatient surgical activities (247 to 136, −45.0%, P = 0.0005) rather than inpatient surgical activities (750 vs. 661, 11.9%, P = 0.02). Length of hospital stay (LOS) increased from 3.3 ± 2.7 days in prepandemic to 5.3 ± 3.9 in the pandemic era (P = 0.004). Among patients with limb-threatening ischemia, the rate of major limb amputation was higher in the pandemic (3.3% vs. 5.4%, respectively, P = 0.02), and a higher rate of elective hospitalization procedures was performed as urgent/emerging setting after clinical deterioration (2.8 % vs. 6.4%, P = 0.0002). According to DRG classification, an increase of “complicated” limb-threatening ischemia (DRG 554) and aortic aneurysm (DRG 110) was observed prepandemic to pandemic (+84.2% and +25.0%, respectively). Total reimbursement for vascular activities between pandemic versus prepandemic was 4,646,108€ vs. 5,054,398€, respectively (−8.0%). Management of “complicated” limb-threatening ischemia (DRG 554) and aortic aneurysm (DRG 110) required a higher clinical and financial support that was translated into higher economic reimbursement during the pandemic (273,035€ vs. 150,005€, +82.0% and 749,250€ vs. 603,680€, +24.1%, respectively).ConclusionsDuring the pandemic, the main resources were employed for the treatment of limb-threatening ischemia, aortic aneurysm, and carotid stenosis. Inpatient activities documented an increase in major limb amputation and LOS. An increased reimbursement for each vascular procedure and for all “complicated” diagnoses revealed that the more serious and resource-demanding pathology occurred in this period.

Evaluation of Machine Learning Techniques to Predict the Likelihood of Mental Health Conditions Following a First mTBI.

ObjectiveLimited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active duty Service Members (SMs) following a first diagnosis of mild traumatic brain injury (mTBI).Materials and MethodsPatient demographics and encounter metadata of 35,451 active duty SMs who have sustained an initial mTBI, as documented within the EHR, were obtained. All encounter records from a year prior and post index mTBI date were collected. Patient demographics, ICD-9-CM and ICD-10 codes, enhanced diagnostic related groups, and other risk factors estimated from the year prior to index mTBI were utilized to develop a feature vector representative of each patient. To embed temporal information into the feature vector, various window configurations were devised. Finally, the presence or absence of mental health conditions post mTBI index date were used as the outcomes variable for the models.ResultsWhen evaluating the machine learning models, neural network techniques showed the best overall performance in identifying patients with new or persistent mental health conditions post mTBI. Various window configurations were tested and results show that dividing the observation window into three distinct date windows [−365:−30, −30:0, 0:14] provided the best performance. Overall, the models described in this paper identified the likelihood of developing MH conditions at [14:90] days post-mTBI with an accuracy of 88.2%, an AUC of 0.82, and AUC-PR of 0.66.DiscussionThrough the development and evaluation of different machine learning models we have validated the feasibility of designing algorithms to forecast the likelihood of developing mental health conditions after the first mTBI. Patient attributes including demographics, symptomatology, and other known risk factors proved to be effective features to employ when training ML models for mTBI patients. When patient attributes and features are estimated at different time window, the overall performance increase illustrating the importance of embedding temporal information into the models. The addition of temporal information not only improved model performance, but also increased interpretability and clinical utility.ConclusionPredictive analytics can be a valuable tool for understanding the effects of mTBI, particularly when identifying those individuals at risk of negative outcomes. The translation of these models from retrospective study into real-world validation models is imperative in the mitigation of negative outcomes with appropriate and timely interventions.

Is value-based payment for healthcare feasible under Ghana\u2019s National Health Insurance Scheme?

BackgroundEffective payment mechanisms for healthcare are critical to the quality of care and the efficiency and responsiveness of health systems to meet specific population health needs. Since its inception, Ghana’s National Health Insurance Scheme (NHIS) has adopted fee-for-service, diagnostic-related groups and capitation methods, which have contributed to provider reimbursement delays, rising costs and poor quality of care rendered to the scheme’s clients. The aim of this study was to explore stakeholder perceptions of the feasibility of value-based payment (VBP) for healthcare in Ghana. Value-based payment refers to a system whereby healthcare providers are paid for the value of services rendered to patients instead of the volume of services.MethodsThis study employed a cross-sectional qualitative design. National-level stakeholders were purposively selected for in-depth interviews. The participants included policy-makers (n = 4), implementers (n = 5), public health insurers (n = 3), public and private healthcare providers (n = 7) and civil society organization officers (n = 1). Interviews were audio-recorded and transcribed. Data analysis was performed using both deductive and inductive thematic analysis. The data were analysed using QSR NVivo 12 software.ResultsGenerally, participants perceived VBP to be feasible if certain supporting systems were in place and potential implementation constraints were addressed. Although the concept of VBP was widely accepted, study participants reported that efficient resource management, provider motivation incentives and community empowerment were required to align VBP to the Ghanaian context. Weak electronic information systems and underdeveloped healthcare infrastructure were seen as challenges to the integration of VBP into the Ghanaian health system. Therefore, improvement of existing systems beyond healthcare, including public education, politics, data, finance, regulation, planning, infrastructure and stakeholder attitudes towards VBP, will affect the overall feasibility of VBP in Ghana.ConclusionValue-based payment could be a feasible policy option for the NHIS in Ghana if potential implementation challenges such as limited financial and human resources and underdeveloped health system infrastructure are addressed. Governmental support and provider capacity-building are therefore essential for VBP implementation in Ghana. Future feasibility and acceptability studies will need to consider community and patient perspectives.

VALIDATION OF A DYNAMIC RISK CLASSIFICATION SYSTEM FOR IN-HOSPITAL DEATH, BASED ON ELECTRONIC RECORDS OF NON-SURGICAL ADMISSIONS TO GENERAL HOSPITALS

Objective: To develop and validate a risk-classification system for in-hospital death, clinically useful for general hospital adult primarily non-surgical cases.&#x0D; Methods: Admissions for non-surgical conditions at 5 public general hospitals of Minas Gerais were included. Procedures: Build a predictive model for death during admission, using logistic regression; Create a severity index based on the independent effect of the selected variables, and then, validate its ability to predict in-hospital death during index admission; Validate the predictive scale by challenging it with a new dataset.&#x0D; Results: The final multivariate model included seven significant predictive variables: age, gender, diagnostic-related group, hospital of index admission, admission to the ICU, total length of stay, and unplanned surgical procedure. This model presented adequate fit and fair discriminative performance (AUC=0.78). Temporal validation with a new sample also presented an adequate fit, and the discriminative performance was again fair (AUC=0.76).&#x0D; Conclusions: A dynamic and clinically useful risk classification system for in-hospital death of non-surgical admissions has been validated.

Relationships between Demographic Factors and Chronic Conditions with Disease Severities.

Disease severities are the outcomes of an inpatient visit classification that assigns a diagnostic related group, including risk of mortality and severity of illness. Although widely used in healthcare, the analysis of factors affecting disease severities has not been adequately studied. In this study, we analyze the relationships between demographics and chronic conditions and specify their influence on disease severities. Descriptive statistics are used to investigate the relationships and the prevalence of chronic conditions. To evaluate the influence of demographic factors and chronic conditions on disease severities, several multinomial logistic regression models are performed and prediction models for disease severities are conducted based on National Inpatient Sample data for 2016 provided by the Healthcare Cost and Utilization Project database in the United States. The rate of patients with a chronic illness is 88.9% and the rate of patients with more than two chronic conditions is 67.6%; further, the rate is 62.7% for females, 73.9% for males, and 90% for the elderly. A high level of disease severity commonly appears in patients with more than two chronic conditions, especially in the elderly. For patients without chronic conditions, disease severities show a lower or safe level, even in the elderly.

Payment mechanism and hospital admission: New evidence from Thailand healthcare reform

In 2007, Thailand's Civil Servant Medical Benefit Scheme (CSMBS), one of the three main public health insurers, adopted a new payment mechanism for hospital admission. There has been a shift from fee-for-service toward Diagnostic Related Group (DRG)-based payment that transfers financial risk from the government to health care providers. This study investigates the effects of this policy change on hospital admission, frequency of admission, length of stay (LOS), type of hospital admitted, and out-of-pocket (OOP) inpatient medical expenditure. By employing nationally representative micro-level data (Health and Welfare surveys) and difference-in-difference approach, this study finds a 1 percentage point decline in hospitalization, a 10% higher chance of admission at community hospitals (the lowest level inpatient public health care facility), and a 7% less chance of admission at higher level public health care facilities like general hospitals. No significant change was observed in LOS, frequency of admission, or OOP inpatient medical expenditure associated with the post-2007 payment mechanism change. Our results emphasize the effectiveness of a close-ended payment mechanism for health care in developing countries. This study also adds to the limited literature on using micro-level data to investigate payment mechanism change in the context of low- and middle-income countries.

Understanding mortality among hematology oncology patients identified from hospital administrative data.

215 Background: Hospital administrative data has large ramifications for quality of care and quality improvement projects. Unexpected inpatient mortality among patients with a hematology/oncology (H/O) diagnostic related group (DRG) is not widely studied and reported. We sought to investigate the unexpected inpatient mortality rate among cancer patients who were admitted to a large, urban, tertiary care safety net teaching hospital. Methods: We obtained the hospital’s adjusted mortality rate and evaluated the subset of specific observed/expected deaths ratio (O/E) of patients with a primary H/O diagnosis. We reviewed each case that was identified as an unexpected inpatient mortality from 2016 to 2018. A chart abstraction tool was designed for data abstraction that included demographics, location of admission, comorbid conditions, if mortality was expected, if documentation was reflective of the severity of the illness. Two independent reviewers abstracted each chart. A third reviewer assessed each case to make a final determination regarding expected mortality based on medical complexity and if documentation was reflective of severity of illness. Results: On review of the inpatient mortality data, the O/E for the H/O diagnostic related group (DRG) from Vizient was 1.14. Twenty two cases were identified as having an unexpected death. Among those cases, 23% of patients were transferred from an outside hospital, and 23% of patients did not have a known cancer diagnosis on admission. In the majority of cases, initial documentation did not accurately reflect severity of illness and/or specialists in hematology or oncology were not consulted at time of diagnosis. We noted the majority of patients were not followed by a hematologist/oncologist within the hospital system (63.6%). In 55% of patients, the cause of death was acute respiratory failure, and 14% of patients had an Advance Directive/DNAR in the chart prior to admission. Delayed antibiotics in febrile neutropenia and a missed blood transfusion reaction were among causes of unexpected deaths. Conclusions: Hospital generated data reported that mortality among H/O patients was greater than projected, however careful chart review of each case demonstrated a significant number of cases that were expected but the severity of illness was not documented properly to account for the death. Templates for proper documentation using the Vizient mortality variables is a key area to lower unexpected inpatient mortality.

Delayed Discharge for Non-Clinical Reasons in Hip Procedures: Differential Characteristics and Opportunity Cost.

Delayed discharge for non-clinical reasons shares common characteristics with hip procedures. We sought to quantify the length of stay and related costs of hip procedures and compare these with other cases of delayed discharge. A cross-sectional study was conducted at a public hospital in Spain (2007–2015) including 306 patients with 6945 days of total stay and 2178 days of prolonged stay. The mean appropriate stay was 15.58 days, and the mean prolonged stay was 7.12 days. The cost of a prolonged stay was €641,002.09. The opportunity cost according to the value of the hospital complexity unit was €922,997.82. The mean diagnostic-related groups’ weight was 3.40. Up to 85.29% of patients resided in an urban area near the hospital (p = 0.001), and 83.33% were referred to a long-stay facility for functional recovery (p = 0.001). The proportion of patients with hip procedures and delayed discharge was lower than previous reports; however, their length of stay was longer. The cost of prolonged stay could account for 21.17% of the total. Compared with the remaining cases of delayed discharge, the appropriate stay was shorter in hip procedures, with a profile of older women living in an urban area close to the hospital and referred to a long-stay center for functional recovery.

French Idiosyncratic Health-Care Reforms, Performance Management and Its Political Repercussions.

ContextFrance has undergone major changes in social policy that made an impact on the health-care sector. The paper reminds readers of the application and shortcomings of the concept of New Public Management (NPM) in the French health system.ProblemThe paper investigates NPM health reforms in France. Reforms aimed at containing costs. What administrative restructuring was implemented? What were reform idiosyncrasies? What were their limitations? Which critical public health emergencies remain?MethodsWe examine the political and economic context of health-care reforms, the rise of the regulatory state, and its core element: the diagnostic-related group (DRG) scale. We critically examine the recentralization of health policy decisions and its impact on care providers and provide an international perspective on reforms.ResultsReforms put priority on the use of yardsticks and also emphasized regulation and competition but rejected public–private partnerships on the Anglo-Saxon model. Central health authorities regain their authority over health policy decisions, and decentralization was weakened.ConclusionRestrictions in public service delivery triggered a general discontent among the population. The political repercussions of reforms eventually crystallized into the Yellow Vest movement.