Healthcare Resource Use Research Articles

Patient use of pulse oximetry to support management of COVID-19 in Greater Manchester: A non-randomised evaluation using a target trial approach.

The pandemic saw widespread use of home pulse oximeters to patients diagnosed with COVID-19 to support early detection of low oxygen saturation levels and appropriate care. Rapid implementation made conventional evaluation challenging, highlighting the need for rigorous non-randomised methods to support decision-making about future use of these technologies. We used routine data to explore the benefits of pulse oximetry in Greater Manchester, under the 'COVID-19 oximetry at home' (CO@h) programme. We used data from the Greater Manchester Secure Data Environment and defined study parameters using a 'target trial' model to compare patients receiving pulse oximetry under the CO@h programme, with matched controls using various comparator groups. Primary outcomes were unplanned hospitalisation and all-cause mortality. This study is based on data from the Greater Manchester Care Record (GMCR), using anonymised, routinely collected data provided in a de-identified format for research. Informed written consent is needed for primary care patient data to be collected for service improvement and research, before data extraction to the GMCR. The study was approved under protocol GMCR RQ-048, on 12/05/2022. As indicated by the University of Manchester ethics decision tool, formal ethical approval was not required for this study. The adjusted odds ratios for an unplanned hospitalisation were higher among patients receiving pulse oximetry: OR 1.86 (95% CI 1.54-2.25) at 28 days, 1.5 (95% CI 1.3-1.74) at 90 days and 1.63 (95% CI 1.44-1.83) at 1 year. Overall odds of mortality were lower among patients receiving pulse oximetry: adjusted ORs of 0.5 (95% CI 0.25-0.98) at 28 days, 0.5 (95% CI 0.32-0.78) at 90 days and 0.58 (95% CI 0.44-0.76) at 1 year. The results were robust to different comparison groups. Use of pulse oximetry at home under the CO@h programme, through the resulting prioritisation for appropriate care, was associated with a higher frequency of unplanned admissions and a reduction in the risk of mortality up to 1 year later. Therefore, it is likely effective for early detection of clinical deterioration and timely intervention among patients with COVID-19. Further research is needed to understand whether this is a cost-effective use of healthcare resources.

Health care utilization and costs among coordinated care patients in Southeastern Ontario: A difference-in-differences study of a double propensity score-matched cohort.

Coordinated care plans (CCPs) for high-cost health care system users aim to improve system-level performance. We evaluated health care resource use and costs among CCP patients (enrollees) versus a control group that did not receive coordinated care (comparators) in Southeastern Ontario. A difference-in-differences analysis of a quasi-experimental, double propensity score-matched and adjusted cohort was conducted. Linked population-based administrative data were used to measure health care utilization and costs and to identify comparators for two enrollee groups who began CCPs between April 1, 2013, and March 31, 2019. Enrollees were recruited from hospitals in Quinte or community care centres in Rural Hastings/Thousand Islands, and were 1:1 propensity score matched to comparators. Difference-in-differences estimates were calculated using generalized estimating equations for hospitalization rates, homecare visits, primary care visits, other health care resources and total costs. A total of 558 enrollees in Quinte and 538 in Rural Hastings/Thousand Islands were identified and matched to comparators. Difference-in-differences estimates were significant in both enrollee groups for number of homecare visits ([IRR 1.72; 95% CI (1.44, 2.06)] and [IRR 1.73; 95% CI (1.45, 2.06)], respectively). Number of primary care visits were 1.76 times greater for Rural Hastings/Thousand Islands enrollees versus comparators [IRR 1.76; 95% CI (1.32, 2.35)]; total costs increased by 23% ([IRR 1.23; 95% CI (1.09,1.39)]. Homecare use significantly increased for enrollees versus comparators, indicating specific priority areas of Ontario CCPs were met. However, no reductions were shown for other health system performance indicators. We also showed increased 7-day primary care follow-up visits for community care centre-recruited patients, but not for hospital-recruited patients. Decision-makers may wish to target patients who are less advanced in their chronic disease trajectory.

Machine Learning in Health Anylitics and Patient Monitoring Solomon Kavuta and Mr Joel Mulepa Dmi St John The Baptist University, Lilongwe, Malawi

The integration of machine learning techniques in healthcare has revolutionized the way predictive analytics and patient monitoring are conducted. This transformation is reshaping traditional healthcare practices by harnessing the power of advanced computational methods to analyze vast amounts of patient data. Machine learning algorithms, which excel at detecting patterns and making predictions, leverage this data to forecast disease onset, progression, and treatment outcomes. This capability enables healthcare providers to adopt proactive measures, facilitating early intervention and the development of personalized treatment plans tailored to individual patients' needs. The potential of machine learning in healthcare is vast and multifaceted. By continuously analyzing real-time patient data streams from various sources such as electronic health records (EHRs), medical imaging, genomic information, and data from wearable devices, these algorithms can identify subtle changes in a patient's condition that may indicate the early stages of a disease or the risk of an adverse event. This continuous monitoring allows for timely interventions that can prevent complications, reduce the severity of diseases, and improve overall patient outcomes. For example, machine learning can help predict the likelihood of a patient developing conditions like diabetes or heart disease, allowing for lifestyle changes and medical interventions that can mitigate these risks before they become severe health issues. Moreover, machine learning facilitates remote patient monitoring, which has become increasingly important in the context of modern healthcare. With the advent of wearable devices and Internet of Things (IoT) sensors, it is now possible to collect and analyze health data outside of traditional clinical settings. These technologies enable healthcare providers to monitor patients in real-time, regardless of their location, ensuring continuous care and oversight. This is particularly beneficial for managing chronic diseases, where regular monitoring can significantly improve patient management and outcomes. For instance, remote monitoring of patients with heart conditions can detect early signs of arrhythmias or other issues, prompting immediate medical responses that can prevent more serious complications. The transformative impact of machine learning on healthcare delivery extends beyond patient monitoring and predictive analytics. It also includes enhancing diagnostic accuracy, optimizing treatment protocols, and streamlining administrative processes. By reducing the need for frequent hospital visits and enabling more efficient use of healthcare resources, machine learning can help lower healthcare costs while improving the quality of care provided to patients. Additionally, the ability to analyze large datasets quickly and accurately can aid in medical research, uncovering new insights into disease mechanisms and potential treatments.

Healthcare costs and resource utilization of patients with chronic post-traumatic stress disorder: a retrospective US claims analysis of commercially insured patients.

Aim: Exploring the healthcare costs and resource use among privately insured US patients with post-traumatic stress disorder (PTSD). Methods: This study used Merative MarketScan data. The index date was defined as the first PTSD claim. Study period included a 1-year pre-index and 2-year post-index follow-up. Cases with only acute PTSD, cancer, or insurance gap during the study period were excluded. The PTSD with (PwC) and PTSD without comorbidities (PwoC) cohorts were defined by the presence/absence of comorbid mental health conditions (schizophrenia, bipolar and major depressive disorder). Baseline PTSD (BP) cohort included PwoC cases with only index PTSD event and without FDA-approved PTSD medications or psychotherapy. Sub-analysis is conducted among patients with PTSD and substance/alcohol use disorder. Study cohorts were matched in a 1:1:1 ratio. Results: The matched sample included 5076 patients (1681 PwC, 1681 PwoC, 1714 BP). PwC patients had higher 2-year PTSD-related costs than PwoC and BP patients ($3762 vs $1750 and $841; all p<0.001). The same trend was noted among all-cause and anxiety-related costs. PwC patients had higher 2-year PTSD-related inpatient and emergency department (ED) rates than PwoC (10.2% vs 1.7% and 6.8% vs 2.6%, all p<0.001) and inpatient and outpatient rates than BP (10.2% vs 2.1% and 98.0% vs 93.1%; all p≤0.004). The sub-analysis had 3776 patients (3154 PwC, 537 PwoC, 85 BP). PwC had higher 2-year PTSD-related costs than PwoC and BP ($7668 vs $2919 and $1,483; all p<0.001). The same trend was noted in all-cause and anxiety-related costs. PwC also had higher 2-year PTSD-related inpatient and ED rates than PwoC (25.6% vs 10.4% and 12.7% vs 5.2%; all p<0.001) and inpatient and outpatient rates than BP (25.6% vs 8.2% and 95.5% vs 84.7%; all p<0.001). Conclusion: PTSD is associated with high healthcare costs and resource use. The highest economic burden was observed in patients with PTSD and mental health comorbidities.

From Clinical to Non-clinical Outcomes in the Treatment of HIV: An Economic and Organizational Impact Assessment.

The aim of this study was to define the economic and organizational impacts related to a broader utilization of bictegravir/emtricitabine/alafenamide (BIC/FTC/TAF) in Italian clinical practice. A budget impact analysis-representing the evolution of the Italian National Healthcare Service (NHS) healthcare expenditure over 3years-was developed, considering the overall Italian population treated for human immunodeficiency virus (HIV). Model input variables were treatment history, therapeutic regimen, development of adverse events, achievement of an undetectable viral load and total direct healthcare costs. Besides the BIA, an organizational impact assessment was conducted to determine the impact on the use of healthcare resources, assessing the release of organizational hospital assets, focusing on the management of drug-related adverse events. Data were collected from scientific evidence, Italian national and regional legislations and healthcare professionals' reports. To verify the robustness of the economic and organizational impact assessment, sensitivity analyses were performed. Results demonstrate economic savings of about 26million euros in total health spending, assuming a higher penetration rate for BIC/FTC/TAF. This change in the current case mix would lead to a reduction in the specific costs related to adverse event management (0.9million euros; -2.09%) and in the medical management of patients (38million euros; -7.79%), with a positive impact on the achievement of virological control. From an organizational perspective, a wider use of BIC/FTC/TAF generates a reduction in the utilization of healthcare resources due to a decrease in adverse events and complications. The model estimated a 19.64% reduction in HIV-related inpatient days, which freed up healthcare professional time. Capable of improving both economic and organizational sustainability for the entire HIV care continuum, BIC/FTC/TAF is an efficient therapeutic strategy for people with HIV.

A systematic literature review on public health and healthcare resources for pandemic preparedness planning.

Generating insights into resource demands during outbreaks is an important aspect of pandemic preparedness. The EU PANDEM-2 project used resource modelling to explore the demand profile for key resources during pandemic scenarios. This review aimed to identify public health and healthcare resources needed to respond to pandemic threats and the ranges of parameter values on the use of these resources for pandemic influenza (including the novel influenza A(H1N1)pdm09 pandemic) and the COVID-19 pandemic, to support modelling activities. We conducted a systematic literature review and searched Embase and Medline databases (1995 - June 2023) for articles that included a model, scenario, or simulation of pandemic resources and/or describe resource parameters, for example personal protective equipment (PPE) usage, length of stay (LoS) in intensive care unit (ICU), or vaccine efficacy. Papers with data on resource parameters from all countries were included. We identified 2754 articles of which 147 were included in the final review. Forty-six different resource parameters with values related to non-ICU beds (n = 43 articles), ICU beds (n = 57), mechanical ventilation (n = 39), healthcare workers (n = 12), pharmaceuticals (n = 21), PPE (n = 8), vaccines (n = 26), and testing and tracing (n = 19). Differences between resource types related to pandemic influenza and COVID-19 were observed, for example on mechanical ventilation (mostly for COVID-19) and testing & tracing (all for COVID-19). This review provides an overview of public health and healthcare resources with associated parameters in the context of pandemic influenza and the COVID-19 pandemic. Providing insight into the ranges of plausible parameter values on the use of public health and healthcare resources improves the accuracy of results of modelling different scenarios, and thus decision-making by policy makers and hospital planners. This review also highlights a scarcity of published data on important public health resources.

Improving equitable healthcare resource use: developing a neighbourhood district nurse needs index for staffing allocation

BackgroundAllocating healthcare resources to local areas in proportion to need is an important element of many universal health care systems, aiming to provide equal access for equal need. The UK National Health Service allocates resources to relatively large areas in proportion to need, using needs-weighted capitation formulae. However, within those planning areas, local providers and commissioners also require robust methods for allocating resources to neighbourhoods in proportion to need to ensure equitable access. We therefore developed a local resource allocation formula for NHS district nursing services for a City in the North West of England, demonstrating a novel application of the national formulae principles for equitable resource allocation to small areas.MethodsUsing linked data from community health services, primary care, secondary care and social care, we used a zero-inflated Poisson regression to model the number of district nursing services contacts for each individual based on predictors of need, while including the supply of district nurses per head to account for historical supply induced patterns. Individual need was estimated based on the predictions from this model, keeping supply fixed at the average. We then compared the distribution of district nurses between neighbourhoods, based on our formula, to the current service staffing distribution.ResultsKey predictors of need for district nursing services were age, deprivation, chronic diseases such as, cardiovascular disease, chronic liver disease, neurological disease, mental ill health, learning disability living in a nursing home, living alone, and receiving palliative care. Need for district nursing services was highly weighted towards older and more deprived populations. The current distribution of staff was, however, more correlated with age than deprivation. Moving to a needs-based staffing distribution would shift staff from less deprived areas to more deprived areas potentially reducing inequalities.ConclusionA neighbourhood-level model for needs for district nursing is a useful tool that can potentially improve the allocation of resources, addressing unmet need and inequalities.

Nationwide Epidemiology and Outpatient Healthcare Resource Use of Children with Respiratory Syncytial Virus from 2005 to 2021.

Respiratory syncytial virus (RSV) poses a global health challenge, particularly among younger children. While the disease burden in Japan has been preliminarily quantified in short-term or inpatient settings, a comprehensive understanding of outpatient settings at a national level is still lacking. In this retrospective cohort study, we followed 697,802 children until they reached 60 months of age, amounting to 25,680,468 million person-months, using two nationally representative databases from the fiscal years 2005-2021. We analyzed trends in the epidemiology of RSV infections and associated outpatient health resource use. Incidence rates of RSV and associated hospitalizations among infants showed fluctuations of 50-100 cases and 20-30 hospitalizations per 1000 person-years, respectively, during the 2010s. These rates dropped to 8.7 cases and 2.2 hospitalizations per 1000 person-years in 2020, then returned to the same levels in the 2010s. Similar patterns were noted for RSV testing, outpatient visits, healthcare cost and the proportion of cases hospitalized (case-hospitalization risk). Whereas antibiotic use decreased from 56.4% in 2005 to 27.8% in 2021, palivizumab use increased from 95.2-195.9 days of therapy per 1000 person-years. Applying the calculated incidence rates to national data, annual outpatient healthcare costs for RSV infections were estimated to be 7-9 billion JPY (50-64 million USD) for children aged <60 months in the late 2010s. Our study highlights the changes in epidemiology and outpatient health resource utilization for children with RSV infections. These findings are valuable for policymakers and clinicians aiming to develop strategies, including newly developed maternal vaccines and single-dose long-acting monoclonal antibodies.

Complications, Costs, and Health Care Resource Use with Tissue Biopsy Followed by Liquid Biopsy Versus Tissue Re-biopsy in Patients With Newly Diagnosed Metastatic Nonsmall-cell Lung Cancer.

We compared complications, costs, and health care resource utilization (HCRU) of patients with newly diagnosed metastatic nonsmall-cell lung cancer (mNSCLC) who had a tissue biopsy followed by either liquid biopsy (TFLB) (identified with a novel algorithm) or tissue re-biopsy (TRB). This claims-based retrospective analysis included commercial and Medicare Advantage members in the Optum Research Database with mNSCLC (January 2017 to June 2021) and ≥2 tissue biopsy claims (7 to 90d apart) (TRB) or ≥1 tissue and ≥1 liquid biopsy claim within 90 days (TFLB). Patients in the TFLB group were matched 1:1 to patients in the TRB group using propensity score matching. Surgical biopsy-related complications and complication-related and all-cause medical costs and HCRU during the 6-month follow-up were compared. Both groups had 235 patients post-match. During the follow-up, the surgical biopsy-related complication rate was lower in the TFLB group than the TRB group (65.1% [153/235] vs. 84.7% [199/235], P<0.001). Mean complication-related medical costs were significantly lower with TFLB ($8494 vs. $19,741, P<0.001) during the follow-up; mean (SD) duration of complication-related inpatient stays was significantly lower with TFLB (3.5 [7.0] vs. 6.6 [13.3]d, P=0.002). Mean all-cause medical costs were not significantly different between the groups; the TFLB group had fewer all-cause inpatient stays, inpatient days, and outpatient visits. Multiple tissue biopsy procedures may be associated with significantly higher biopsy complication rates, higher complication-related medical costs, and longer complication-related inpatient stays than TFLB. All-cause medical costs were similar between groups.

Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging

This study was performed to investigate the diagnostic value of radiomics models constructed by fat suppressed T2-weighted imaging (T2WI-FS) and contrast-enhanced T1-weighted imaging (CET1) based on magnetic resonance imaging (MRI) for differentiation of osteosarcoma (OS) and chondrosarcoma (CS). In this retrospective cohort study, we included all inpatients with pathologically confirmed OS or CS from Second Xiangya Hospital of Central South University (Hunan, China) as of October 2020. Demographic and imaging variables were extracted from electronic medical records and compared between OS and CS group. Totals of 530 radiomics features were extracted from CET1 and T2WI-FS sequences based on MRI. The least absolute shrinkage and selection operator (LASSO) method was used for screening and dimensionality reduction of the radiomics model. Multivariate logistic regression analysis was performed to construct the radiomics model, and receiver operating characteristic curve (ROC) was generated to evaluate the diagnostic accuracy of the radiomics model. The training cohort and validation cohort included 87 and 29 patients, respectively. 8 CET1 features and 15 T2WI-FS features were screened based on the radiomics features. In the training group, the area under the receiver-operator characteristic curve (AUC) value for CET1 and T2WI-FS sequences in the radiomics model was 0.894 (95% CI 0.817–0.970) and 0.970 (95% CI 0.940–0.999), respectively. In the validation group, the AUC value for CET1 and T2WI-FS sequences in the radiomics model was 0.821 (95% CI 0.642–1.000) and 0.899 (95% CI 0.785–1.000), respectively. In this study, we developed a radiomics model based on T2WI-FS and CET1 sequences to differentiate between OS and CS. This model exhibits good performance and can help clinicians make decisions and optimize the use of healthcare resources.

Recurrence of Cardiovascular Events After an Acute Myocardial Infarction in Patients with Multivessel Disease and Associated Healthcare Costs: A German Claims Data Analysis.

This study sought to quantify the healthcare costs of multivessel disease (MVD) and determine the prevalence and incidence of recurrent major adverse cardiovascular events (MACE) in high-risk patients diagnosed with MVD following an acute myocardial infarction (MI). This retrospective study utilized German claims data (AOK PLUS), between 01/01/2010 and 31/12/2020. Patients were included if they (1) had an inpatient diagnosis of an MI between 01/01/2012 and 31/12/2019 (index date), (2) were ≥ 18 years of age at date of MI diagnosis, and (3) had diabetes or met two of the following criteria: ≥ 65 years old, prior MI, peripheral arterial disease. MACE was defined as (1) MI, (2) stroke, or (3) death with a cardiovascular diagnosis within 30 days prior. To measure the burden of MVD, patients were identified during the index hospitalization by presence of MVD. Healthcare resource use and costs were compared after adjustment based on propensity score matching (PSM). A total of 5158 patients with evidence for MVD were included in the main analysis. 31.17% experienced a MACE within 365 days following the incident MI. After PSM adjustment, 33.22% of the MVD cohort experienced a MACE versus 36.48% of non-MVD patients. MVD patients had a higher rate of recurrent MI (14.22% vs. 9.81%). Additionally, public healthcare costs were about €4 million higher in the total MVD cohort than in the non-MVD cohort in the first year after an MI (€47,896,012.32 vs. €43,718,713.75, respectively), reflecting the MVD cohort's higher use of the public healthcare system. More MVD patients were prescribed guideline-recommended medication (61.4% vs. 46.0%). This study found that presence of MVD contributed to higher rates of recurrent MI. Patients with MVD experienced higher rates of recurrent MI despite a higher proportion of patients receiving guideline-directed medication therapy compared to non-MVD patients. Conversely, there was a higher mortality rate observed in the non-MVD cohort.

Health care resource use in preschool children with cerebral palsy.

To estimate the burden of disease and evaluate which factors affect health care resource use (HCRU) in young children with cerebral palsy (CP). Data were collected as part of a prospective, longitudinal cohort study of children with CP born in Queensland, Australia between 2006 and 2009. HCRU questionnaires were administered at six time points. Data on resource use, socio-demographics, and disease severity were collected. Costs were sourced from Medicare, the Australian National Hospital Cost Data Collection, and market prices. A generalized linear model was used to identify factors influencing CP-related costs. A total of 794 questionnaires were completed by 222 participants (mean = 3.6 per participant). Physiotherapy (94%, n = 208) was the most widely accessed allied health care therapy; almost half of the participants (45%; 354 of 794) reported one or more hospital admissions. From the health care funder perspective, a child with CP costs on average A$24 950 per annum (A$12 475 per 6 months). Higher costs were associated with increased motor impairment (Gross Motor Function Classification System, p < 0.001) and increased comorbidities (p = 0.012). HCRU in preschool children with CP can be analysed according to disease severity. Both increased motor impairments and increased comorbidities were associated with higher health care costs. Children with a higher number of comorbidities had higher health care costs, and more specifically, higher hospitalization costs. No significant change in costs was found over time as children aged from 18 months to 5 years.

Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50–60, 60–70, and 70–80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Bagging-RF integrated model with eight other machine learning classifiers. Finally, the model’s performance was evaluated by accuracy, F1 score, and other metrics. The results showed that the Bagging-RF model outperformed the other eight machine learning classifiers, exhibiting 97.35%, 95.55%, 95.14% accuracy and 97.35%, 97.35%, 95.14% F1 Score at the Diabetes Prediction Dataset for diabetes prediction for the three age groups of 50–60, 60–70, and 70–80; and 97.03%, 94.90%, 93.70% accuracy and 97.03%, 94.90%, 93.70% F1 Score at the Diabetes Prediction Dataset. 95.55%, 95.13% F1 Score; and 97.03%, 94.90%, 93.70% accuracy; and 97.03%, 94.89%, 93.70% F1 Score at Diabetes Prediction Dataset. In addition, while other integrated learning models, such as ET, RF, Adaboost, and XGB, fail to outperform Bagging-RF, they also show excellent performance.

Cost-Effectiveness Analysis of Pharmaceutical-Grade Chondroitin Sulfate for Knee Osteoarthritis Based on Individual Patient Data from a Randomized Clinical Trial.

In a previously published randomised, placebo-controlled trial, 800mg/day of pharmaceutical-grade chondroitin sulfate (CS) was shown to be superior to placebo in reducing pain and improving function over 6months in patients with symptomatic knee osteoarthritis (OA). The aim of the current posthoc analyses was to evaluate the cost-effectiveness of CS compared with placebo in a European perspective using individual patient data from this clinical trial. Patients with knee OA randomised to CS or placebo were followed up at 1, 3 and 6months. The algo-functional Lequesne index was used to derive the EuroQol Five-Dimension Five-Level (EQ-5D-5L) score based on a validated formula. The EQ-5D-5L scores at each time point were used to calculate the changes in quality-adjusted life years (QALYs) with the area under the curve method. Costs were assessed using the average price of CS in the countries where the original study took place and where CS is currently marketed. The costs of CS in three countries were then used (i.e. the Czech Republic, Italy and Switzerland). The incremental cost-effectiveness ratio (ICER) threshold for CS to be considered cost-effective was set at 91,870EUR per QALY (equivalent to the usually recommended threshold of US$100,000). The study used an intention-to-treat population, i.e. patients who received one dose of the study drug, and imputed missing values using the basal observation carried forward method. No significant differences in baseline characteristics were observed between the CS group (N = 199) and the placebo group (N = 205). The mean cost of CS for 6months of treatment was 194.74EUR. After 6months of treatment, CS showed a mean ICER of 33,462 (95%CI 5130-61,794) EURper QALY gained, indicating cost-effectiveness compared with placebo. The acceptability curve for cost-effectiveness shows that the CS treatment is likely to be cost-effective compared with placebo, with a 93% probability when the ceiling ratio is set at 91,870EUR per QALY gained. These results highlight the role of CS as a cost-effective therapeutic option in the management of OA. However, further studies taking into account the use of other healthcare resources are warranted for a more complete understanding.

Low-Value Clinical Practices in Pediatric Trauma Care

Reducing low-value care has the potential to improve patient experiences and outcomes and decrease the unnecessary use of health care resources. Research suggests that low-value practices (ie, the potential for harm exceeds the potential for benefit) in adult trauma care are frequent and subject to interhospital variation; evidence on low-value practices in pediatric trauma care is lacking. To estimate the incidence of low-value practices in pediatric trauma care and evaluate interhospital practice variation. A retrospective multicenter cohort study in a Canadian provincial trauma system was conducted. Children younger than 16 years admitted to any of the 59 provincial trauma centers from April 1, 2016, to March 31, 2022, were included. Low-value practices were identified from systematic reviews of clinical practice guidelines on pediatric trauma. The frequencies of low-value practices were evaluated by estimating incidence proportions and cases per 1000 admissions (low if ≤10% and ≤10 cases, moderate if >10% or >10 cases, and high if >10% and >10 cases) were identified. Interhospital variation with intraclass correlation coefficients (ICCs) were assessed (low if <5%, moderate if 5%-20%, and high if >20%). A total of 10 711 children were included (mean [SD] age, 7.4 [4.9] years; 6645 [62%] boys). Nineteen low-value practices on imaging, fluid resuscitation, hospital/intensive care unit admission, specialist consultation, deep vein thrombosis prophylaxis, and surgical management of solid organ injuries were identified. Of these, 14 (74%) could be evaluated using trauma registry data. Five practices had moderate to high frequencies and interhospital variation: head computed tomography in low-risk children (7.1%; 33 per 1000 admissions; ICC, 8.6%), pretransfer computed tomography in children with a clear indication for transfer (67.6%; 4 per 1000 admissions; ICC, 5.7%), neurosurgical consultation in children without clinically important intracranial lesions (11.6%; 13 per 1000 admissions; ICC, 15.8%), hospital admission in isolated mild traumatic brain injury (38.8%; 98 per 1000 admissions; ICC, 12.4%), and hospital admission in isolated minor blunt abdominal trauma (10%; 5 per 1000 admissions; ICC, 31%). In this cohort study, low-value practices appeared to be frequent and subject to interhospital variation. These practices may represent priority targets for deimplementation interventions, particularly as they can be measured using routinely collected data.

Impact of heart failure diagnosis on recorded healthcare resource use by older people in Wales; is primary care shouldering a hidden burden?

Abstract Background Heart failure (HF) is associated with high healthcare resource use and mortality. Most of the evidence focuses on hospital admission data. The impact on primary care interactions and outpatient attendance is less well described, but this information is important in understanding resource utilisation, patient pathways, and in planning services. Purpose To examine how a diagnosis of HF impacts healthcare resource use and outcomes for older people in Wales. Methods We conducted a retrospective, population-level, observational study using linked anonymised electronic health record (EHR) data in Wales (2015-22). 737,230 individuals were aged 65y+ in the study period in primary and secondary care data. Of these, 65,010 had a code for HF in their primary or secondary care records. To evaluate changes in resource use, we excluded 13,990 individuals with fewer than 12 months health data pre- and post-diagnosis. We summarised general practice (GP) interactions, emergency department (ED) attendance, outpatient (OP) attendance, inpatient (IP) episodes and days, time between HF diagnosis and death, and place of death. Results The final cohort comprised 51,020 individuals 65y and over with a diagnosis of HF between 2015-2022. Incidence and prevalence of HF increased across the study period (1.4-1.5% and 8.5-9.1% respectively). Figure 1 shows the percentage change in healthcare resource use for the cohort when HF was diagnosed (12 months pre-and post-diagnosis comparison). Table 1 shows the average number of healthcare utilisation events per person. After a diagnosis of HF, there are fewer ED attendances (-28.2%) and IP admissions (-9.3%) but more OP appointments (31%) and GP interactions (20.3%). When admissions occur, hospital stays tend to be longer (55.4%). Median time (IQR) from HF diagnosis to death from any cause was 247(34-840) days, and with HF listed as a cause 186(25-780) days. In this cohort, the place of death was recorded as NHS establishment (20,379, 67.8%), community (5,450, 18.1%), non-NHS establishment (3,789,12.6%) and unknown (450, 1.5%). Conclusions In Wales, a diagnosis of HF in those 65y+ increases primary care interactions and OP attendances. The change in volume of primary care interactions is considerable (increase from 1,047,920 to 1,315,080). Longer hospital stays may suggest difficulty in managing patients or discharging back to community settings. Majority of those with HF die in NHS establishments. It is unclear whether this reflects deficits in end-of-life care. These results are important in planning effective health service delivery and in establishing baseline data from which to monitor quality improvements.

Digital health in the EP lab: routine implementation of remote access and technical support for complex ablation procedures

Abstract Background Complex electrophysiological procedures are performed using three-dimensional (3D) electroanatomical mapping (EAM) systems, which are usually operated on site, by field technical engineers (FTE). Due to a lack of skilled personnel, the engineers usually must travel between different hospitals and the procedures have to be planed in advance. Remote access (RA) technologies were recently implemented in clinical practice, aiming to increase the flexibility in the EP lab and maximise healthcare resources use. Methods Electroanatomical mapping was performed remotely by means of an integrated audio-visual solution for RA. The operator and the FTE communicated exclusively using the RA system, while the latter worked only in home office. Consecutive procedures were prospectively enrolled and analysed in terms of indication, procedural characteristics, feasibility and saved travelled time by the FTA. Results Between September 2022 and July 2023, a total of 133 EP procedures were performed in a high volume, tertiary electrophysiological centre in Germany. The mean age was 67 (57.5, 75.5) years. Women represented 39.8% of the population. The median BMI was 26.2 (23.1, 30.3) Kg/m2. A total of 56 (42.1%) patients underwent an atrial fibrillation ablation procedure (37.5% paroxysmal), 22 (16.5%) an atrial tachycardia ablation, 13 (9.8%) cavotricuspid isthmus ablation, 15 (11.3%) supraventricular tachycardia ablation, 14 (10.5%) a premature ventricular contraction ablation, 12 (9%) ventricular tachycardia ablations and 1 patient an electrophysiological diagnostic study. The median time length of procedure was 130 (96.5, 168) minutes. The median number of radiofrequency applications was 63.5 (22.3, 109.5), while the median RF time was 14.2 (6.9, 25.1) minutes. The fluoroscopy time was 9.2 (6.5, 12.6) minutes, with a median radiation dose of 408.5 (233.0, 631.8) cGy x cm2. The median amount of contrast media was 40 (8.75, 50.0) ml and the median amount of heparin administered was 12000 (8000, 16000) UI. Protamine was used in 9 cases, with a median of 7000 (5000, 7000) UI. Except for one supraventricular procedure, where no arrhythmia induction was possible, all procedures were successful. One major complication was observed. In one case, the internet-based headphone connection was not stable. Therefore, the communication was established via a conventional smartphone. No change to on-site support was needed. The travel time reduction was available in 130 cases. In total, 290 travel hours could be saved. Conclusion The routine implementation of RA for complex EP procedures proved to be safe and efficient. This approach has the potential to increase the feasibility in case of emergency procedures, reduce travel times for FTE and streamline healthcare resources allocation.

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

Cost-utility analysis of adapted problem adaptation therapy for depression in mild-to-moderate dementia caused by Alzheimer's disease: PATHFINDER randomised controlled trial.

Depression is common in people with dementia, and negatively affects quality of life. This paper aims to evaluate the cost-effectiveness of an intervention for depression in mild and moderate dementia caused by Alzheimer's disease over 12 months (PATHFINDER trial), from both the health and social care and societal perspectives. A total of 336 participants were randomised to receive the adapted PATH intervention in addition to treatment as usual (TAU) (n = 168) or TAU alone (n = 168). Health and social care resource use were collected with the Client Service Receipt Inventory and health-related quality-of-life data with the EQ-5D-5L instrument at baseline and 3-, 6- and 12-month follow-up points. Principal analysis comprised quality-adjusted life-years (QALYs) calculated from the participant responses to the EQ-5D-5L instrument. The mean cost of the adapted PATH intervention was estimated at £1141 per PATHFINDER participant. From a health and social care perspective, the mean difference in costs between the adapted PATH and control arm at 12 months was -£74 (95% CI -£1942 to £1793), and from the societal perspective was -£671 (95% CI -£9144 to £7801). The mean difference in QALYs was 0.027 (95% CI -0.004 to 0.059). At £20 000 per QALY gained threshold, there were 74 and 68% probabilities of adapted PATH being cost-effective from the health and social care and societal perspective, respectively. The addition of the adapted PATH intervention to TAU for people with dementia and depression generated cost savings alongside a higher quality of life compared with TAU alone; however, the improvements in costs and QALYs were not statistically significant.

Cross-cultural adaptation and validation of the Pittsburgh Sleep Quality Index (PSQI) in the Igbo language

BackgroundSleep disturbances are associated with decreased quality of life, increased use of healthcare resources, and poor mood. The Pittsburgh Sleep Quality Index (PSQI) is a standardised questionnaire that was created to help measure sleep quality and to alert medical professionals to the need for additional evaluation of patients exhibiting sleep-related complaints in seven domains based on the previous month. The non-availability of the Igbo version can affect its effective use among Igbo speakers in Southeast Nigeria.AimTo translate and cross-culturally adapt the PSQI scale to the Igbo Culture and Environment among Igbo language speakers in Southeast Nigeria using established guidelines.MethodThe PSQI was translated, combined, back-translated, and reviewed by an expert panel, leading to the development of the final Igbo version (I-PSQI). A sample size of 100 was calculated using G-power, and the PSQI and I-PSQI were distributed to 100 apparently healthy adults (40% males and 60% females) recruited conveniently in Otolo Nnewi, Nigeria. The data were analysed using frequency counts, percentages, means, standard deviations, Spearman rank order tests, Cronbach’s alpha, and principal component analysis (PCA). The level of significance was set at 0.05.ResultsThere was no statistically significant difference observed between the PSQI and I-PSQI scores (p < 0.001). The correlations between the corresponding domains and total scores on both versions of the PSQI were excellent (ρ = 0.86–1.0). The Cronbach’s alpha value of the I-PSQI was 0.7, indicating acceptable internal consistency between the items on the I-PSQI.ConclusionThe I-PSQI proved to be a valid and reliable instrument, making it suitable for assessing sleep quality among Igbo language speakers.