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Abstract Background Single-pill combination (SPC) of three antihypertensive drugs has been shown to improve adherence to therapy compared with free combinations, but little is known about its long-term costs and health consequences. This study aimed to evaluate the lifetime cost-effectiveness profile of a three-drug SPC of an angiotensin-converting enzyme inhibitor, a calcium-channel blocker, and a diuretic vs the corresponding two-pill administration (a two-drug SPC plus a third drug separately) from the Italian payer perspective. Methods A cost-effectiveness analysis was conducted using multi-state semi-Markov modeling and microsimulation. Using the healthcare utilization database of the Lombardy Region (Italy), 30,172 and 65,817 patients aged ≥ 40 years who initiated SPC and two-pill combination, respectively, between 2015 and 2018 were identified. The observation period extended from the date of the first drug dispensation until death, emigration, or December 31, 2019. Disease and cost models were parametrized using the study cohort, and a lifetime microsimulation was applied to project costs and life expectancy for the compared strategies, assigning each of them to each cohort member. Costs and life-years gained were discounted by 3%. Probabilistic sensitivity analysis with 1,000 samples was performed to address parameter uncertainty. Results Compared with the two-pill combination, the SPC increased life expectancy by 0.86 years (95% confidence interval [CI] 0.61–1.14), with a mean cost differential of -€12 (95% CI -9,719–8,131), making it the dominant strategy (ICER = -14, 95% CI -€15,871–€7,113). The cost reduction associated with the SPC was primarily driven by savings in hospitalization costs, amounting to €1,850 (95% CI 17–7,813) and €2,027 (95% CI 19–8,603) for patients treated with the SPC and two-pill combination, respectively. Conversely, drug costs were higher for the SPC (€3,848, 95% CI 574–10,640 vs. €3,710, 95% CI 263–11,955). The cost-effectiveness profile did not significantly change according to age, sex, and clinical status. Conclusions The SPC was projected to be cost-effective compared with the two-pill combination at almost all reasonable willingness-to-pay thresholds. As it is currently prescribed to only a few patients, the widespread use of this strategy could result in benefits for both patients and the healthcare system.

Abstract Background This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases. Methods A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region. Results The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals’ texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals’ texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas. Conclusions The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

Abstract Background Care pathways are increasingly being used to enhance the quality of care and optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools were insufficient to consider a phenomenon with such high variability adequately and have to be integrated with novel data mining techniques suitable for identifying patterns in complex data structures. Data-driven techniques can potentially support empirically identifying effective care sequences by extracting them from data collected routinely. The purpose of this study is to perform a state sequence analysis (SSA) to identify different patterns of treatment and to asses whether sequence analysis may be a useful tool for profiling patients according to the treatment pattern. Methods The clinical application that motivated the study of this method concerns the mental health field. In fact, the care pathways of patients affected by severe mental disorders often do not correspond to the standards required by the guidelines in this field. In particular, we analyzed patients with schizophrenic disorders (i.e., schizophrenia, schizotypal or delusional disorders) using administrative data from 2015 to 2018 from Lombardy Region. This methodology considers the patient’s therapeutic path as a conceptual unit, composed of a succession of different states, and we show how SSA can be used to describe longitudinal patient status. Results We define the states to be the weekly coverage of different treatments (psychiatric visits, psychosocial interventions, and anti-psychotic drugs), and we use the longest common subsequences (dis)similarity measure to compare and cluster the sequences. We obtained three different clusters with very different patterns of treatments. Conclusions This kind of information, such as common patterns of care that allowed us to risk profile patients, can provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged to prevent the decline of mental health status at the population level.

Abstract Background The healthcare sector is an interesting target for fraudsters. The availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques, making the auditing process more efficient and effective. This research has the objective of developing a novel data mining model devoted to fraud detection among hospitals using Hospital Discharge Charts (HDC) in Administrative Databases. In particular, it is focused on the DRG upcoding practice, i.e., the tendency of registering codes for provided services and inpatients health status so to make the hospitalization fall within a more remunerative DRG class. Methods We propose a two-step algorithm: the first step entails kmeans clustering of providers to identify locally consistent and locally similar groups of hospitals, according to their characteristics and behavior treating a specific disease, in order to spot outliers within this groups of peers. An initial grid search for the best number of features to be selected (through Principal Feature Analysis) and the best number of local groups makes the algorithm extremely flexible. In the second step, we propose a human-decision support system that helps auditors cross-validating the identified outliers, analyzing them w.r.t. fraud-related variables, and the complexity of patients’ casemix they treated. The proposed algorithm was tested on a database relative to HDC collected by Regione Lombardia (Italy) in a time period of three years (2013-2015), focusing on the treatment of Heart Failure. Results The model identified 6 clusters of hospitals and 10 outliers among the 183 units. Out of those providers, we report the in depth the application of Step Two on three Hospitals (two private and one public). Cross-validating with the patients’ population and the hospitals’ characteristics, the public hospital seemed justified in its outlierness, while the two private providers were deemed interesting for a further investigation by auditors. Conclusions The proposed model is promising in identifying anomalous DRG coding behavior and it is easily transferrable to all diseases and contexts of interest. Our proposal contributes to the limited literature regarding behavioral models for fraud detection, identifying the most ’cautious’ fraudsters. The results of the first and the second Steps together represent a valuable set of information for auditors in their preliminary investigation.

Abstract Background Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. Methods Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. Results We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). Conclusions The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.