OBJECTIVES:To explore short-term mortality and its predictors among older patients hospitalized in a acute geriatric ward (AGW) in Northwestern Italy. DESIGN:Retrospective observational single-center cohort study. MATERIAL AND METHODS:Patients consecutively admitted for any reason between June 2021 and May 2022 were included in the analysis. Along with sociodemographic, clinical, and functional variables, prognosis estimation (Palliative Prognostic Index; PPI) at the time of admission was registered. Short-term all-cause mortality (in-hospital and within 3 months of discharge) was the primary outcome. RESULTS:About one-third of the total sample died in the short-term (32.4 %). Along with PPI score (OR 1.115, 95 %CI 1.034-1.202), short-term mortality was independently associated with functional dependency (OR 1.278, 95 %CI 1.170-1.395). CONCLUSIONS:The high short-term mortality in our sample should call for the inclusion of palliative prognostic tools within the in-hospital comprehensive geriatric assessment to better recognize and appropriately manage older patients at the end of life.

The large availability of hospital administrative and clinical data has encouraged the application of Process Mining techniques to the healthcare domain. Predictive Process Monitoring techniques can be used in order to learn from these data related to past historical executions and predict the future of incomplete cases. However, some of these data, possibly the most informative ones, are often available in natural language text, while structured information—extracted from these data—would be more beneficial for training predictive models. In this paper we focus on the scenario of the Home Hospitalization Service, supporting the team in making decisions on the home hospitalization of a patient, by predicting whether it is likely that a new patient will successfully undergo home hospitalization. We aim at investigating whether, in this scenario, we can take advantage of mapping unstructured textual diagnoses, reported by the doctor in the Emergency Department, into structured information, as the standardized disease ICD-9-CM codes, to provide more accurate predictions. To this aim, we devise two different approaches involving respectively lexicographic and semantic distance for mapping textual diagnoses in ICD-9-CM codes and leverage the structured information for making predictions.

The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.

Providing quality hospital services, especially when decisions need to be made, highly depends on the suitability and efficiency of the underlying processes, as well as on the capability of monitoring, analysing and using the data of process executions so as to provide operational support to decision makers. Process Mining can be a useful instrument in this setting. In this extended abstract we report about a real-life healthcare scenario, that is supporting the Home Hospitalization Service Team of an Italian hospital in making decisions about the home hospitalization of patients. We sketch the high-level idea of a solution leveraging Natural Language Processing and Process Mining for achieving the goal and report about some preliminary results, as well as about criticalities and challenges arisen so far.

In a context of digitalization and modernization of healthcare, automatic analysis of clinical data plays a leading role in improving the quality of care. Since much of the information lies in an unstructured form within clinical notes, it is necessary to make use of modern Natural Language Processing techniques to extract and build structured knowledge from the data. However, clinical texts pose unique challenges due to the extensive usage of i) acronyms, ii) non-standard medical jargons and iii) typos over technical terms. In this paper, we present a prototype spell-checker specifically designed for medical texts written in Italian.

This paper explores a modeling and simulation framework in healthcare following a process-centric approach. We focus on an innovative hospital service, Hospital at Home. The framework introduces a business process analysis to detect weakness and bottlenecks, including simulation to perform scenario analysis and the integration to e-Health solutions. The aim is to investigate the role of technological innovations as telemedicine and televisiting in order to explore the impact on both patient well-being and business process management. This work provides an overview of the functioning of the actual process. The outcome of the here proposed framework allows hospital managers to evaluate the extension of this kind of healthcare service, e.g. to other hospital departments or territorial contexts. The direct and positive impact of assistive technologies on the quality of life of patients, has an impact on the overall management of the organizational processes. First, we describe how this kind of service, its presence on the territory, its adaptability and the interaction with telemedicine improves benefits both for patients and operators. Second, we discuss how the approach is suitable to be applied in case of emergency situation, e.g. COVID-19.

Hospital at Home (HaH) has been proposed as a solution to relieve pressure on hospital beds during the COVID-19 pandemic; however, caregivers' feelings of inadequacy and concerns on the need for tighter clinical monitoring might lead to unnecessary and potentially harmful hospital admissions in frail older patients with mild or atypical COVID-19. Here we report the case of a 91-year old woman with severe dementia and atypical COVID-19 that could be successfully managed by our HaH thanks to her highly motivated caregivers and the support of a telemedicine solution (TMS) to provide caregiver training and support as well as supplementary telemonitoring. Despite some well-known issues on TMS use, the hybrid in-person and tele-visit approach of TMS-assisted HaH could help to create a "secure" environment, empowering caregivers to manage frail older adults with COVID-19 at home, avoiding unnecessary admissions to closed wards and their negative physical, functional and psychological outcomes.

Assistive technologies can have a direct and positive impact on the life of users, because they allow patients to be hospitalized in the comfort of their own home while still receiving high quality care from the clinical team and their caregivers. This article outlines the development of a digital assistant equipped with voice and multimedia capabilities for patients hospitalized at home. Following a first phase of requirements' gathering and exploration of user needs, we developed a voice app (e.g. an Alexa Skill) that provides answers in a multi-medial format to patients and caregivers via a smart speaker equipped with a screen. Preliminary results of the experimentation show that the digital assistant was perceived as useful by the patients and their caregivers, even though they also found some limitations that could lead to improvements in the future.

The large availability of hospital administrative and clinical data has encouraged the application of Process Mining techniques to the healthcare domain. Predictive Process Monitoring techniques can be used in order to learn from these data related to past historical executions and predict the future of incomplete cases. However, some of these data, possibly the most informative ones, are often available in natural language text, while structured information - extracted from these data - would be more beneficial for training predictive models. In this paper we focus on the scenario of the Home Hospitalization Service, supporting the team in making decisions on the home hospitalization of a patient, by predicting whether it is likely that a new patient will successfully undergo home hospitalization. We aim at investigating whether, in this scenario, we can take advantage of mapping unstructured textual diagnoses, reported by the doctor in the Emergency Department, into structured information, as the standardized disease ICD-9-CM codes, to provide more accurate predictions. To this aim, we devise an approach for mapping textual diagnoses in ICD-9-CM codes and leverage the structured information for making predictions.

This paper reports the case of a patient with a long-standing history of Alzheimer's Disease with dysphagia and total functional dependence, and a left-arm fracture one month before hospitalization with consequently bedrest.

We would like to report the case of an Alzheimer Disease patient affected by Covid-19 admitted to the Hospital at Home Service (HHS) of the University Teaching Hospital of Turin, Italy. HHS is a multidisciplinary service operating 7 days a week that can be directly activated by hospital wards to allow early and supported discharge from hospital. Four doctors and 14 nurses operate 7 days a week and look after 25 patients a day, on average. The HHS provides substitutive hospital-at-home care in a ''clinical unit'' model. Several examinations and treatments can be carried out at home, including blood tests, electrocardiogram, spirometry, pulse oximetry, ultrasonographic investigations, placement of peripherally inserted central catheters, oxygen, and other respiratory therapies, intravenous fluids and drugs, blood transfusions, surgical treatment of pressure ulcers.1Fabris F Molaschi M Aimonino N et al.Home care for demented subjects: new models of care and home-care allowance.Arch Gerontol Geriatr Suppl. 2004; : 155-162https://doi.org/10.1016/j.archger.2004.04.022Crossref PubMed Scopus (10) Google Scholar, 2Aimonino Ricauda N Tibaldi V et al.Substitutive "hospital at home" versus inpatient care for elderly patients with exacerbations of chronic obstructive pulmonary disease: a prospective randomized, controlled trial.J Am Geriatr Soc. 2008; 56: 493-500Crossref PubMed Scopus (122) Google Scholar, 3Tibaldi V Isaia G Scarafiotti C et al.Hospital at home for elderly patients with acute decompensation of chronic heart failure: a prospective randomized controlled trial.Arch Intern Med. 2009; 169: 1569-1575Crossref PubMed Scopus (91) Google Scholar, 4Isaia G Astengo MA Tibaldi V et al.Delirium in elderly home-treated patients: a prospective study with 6-month follow-up.Age. 2009; 31: 109-117Crossref PubMed Scopus (33) Google Scholar, 5Ricauda NA Tibaldi V Bertone P et al.The RAD-HOME project: a pilot study of home delivery of radiology services.Arch Intern Med. 2011; 171 (10): 1678-1680Crossref PubMed Scopus (18) Google Scholar On march 15th, an 83-year-old woman was admitted to the emergency department from her private home with mild fever (100,4°F) and drowsiness. Her medical history included only a long-standing history of Alzheimer disease with dysphagia and total functional dependence, and a left arm fracture one month prior to hospitalization. In terms of her mental status, caregivers reported that she was aware of her surroundings at her home, while at hospital admission and during the hospital length of stay she was minimally conscious. She was not able to swallow food and drink, neither to swallow medications. Blood pressure at admission was 80/50 mmHg; she had low oxygen blood saturation (SpO2 90%) without dyspnea or cough. Other blood values were the following: WBC 15070 cells/mm3, hemoglobin 15.4 g/dL, creatinine 1.81 mg/dL, PCR 89 mg/L. Chest X-Ray was negative for pneumonia. While she did not have any history of exposure to COVID, she underwent nasopharyngeal swab which resulted negative. She was then moved to an Acute Medical Ward, where she was treated with cephalosporin and fluid supplementation with marginal improvement of clinical conditions (oxygen supplementation was stopped) and blood chemistries (creatinine 1.01 mg/dL, PCR 59 mg/L), but persistently high WBC count (14310 cells/mm3). On March 23rd, the patient returned home with the support of HHS program. On March 27th a new episode of blood oxygen saturation (SpO2 82%–88%) occurred, associated with diarrhea but without fever. A second nasopharyngeal swab was done, which was positive for Covid-19 infection. According to recommendations from an infectious disease specialist, the patient was treated only with supportive measures, including oxygen supplementation, parenteral nutrition, low-dose heparin, and corticosteroids (betamethasone 8 mg twice daily). It was not possible to administer hydroxicloroquine due to an inability to swallow, while the nasogastric tube was not considered appropriate. On April 10th, the patient is still alive and hemodynamically stable. Along with other cases we have cared for, this case has several clinical implications. First, older patients with dementia and Covid-19 infection may present with mild and atypical symptoms, i.e., namely diarrhea or drowsiness. Second, a negative nasopharyngeal swab does not exclude COVID-19 infection if there is a high clinical suspicion. Third, although these frail older patients have reduced chances to survive this infection, adequate supportive measures may improve survival even without the use of targeted therapies of uncertain and unproven benefit. Some of these patients may die for final worsening of general health status during Covid-19 infection rather than for the infection itself. Indeed, in frail, bedridden patients with dementia, poor nutrition, dehydration, and other clinical complications occur commonly during even mild infective illnesses, and are well recognized risk factors for accelerated worsening health status and death, without the provision of adequate supportive measures. Hopefully, immediate activation of such supportive measures in infected patients with mild disease and without indications for hospital admission might reduce the high mortality rates for Covid-19 infection which are increasingly reported from most long term facilities. Gianluca Isaia, Vittoria Tibaldi and Cristina Tamone collected the data and provided substantial contributions to the conception of the work. Writing support was provided by Gianluca Isaia assisted by Mario Bo. Gianluca Isaia, Renata Marinello and Mario Bo revised the paper critically for important intellectual content. The authors report no funding to disclose or conflicts with any product mentioned or concept discussed in this article.

: This article introduces a framework to integrate Business Process Management and Simulation to e-Health solutions in the context of dehospitalization. Assistive technologies clearly have a direct and positive impact on the quality of life of patients, but they also improve the overall management of the organizational processes. In the framework of business process analysis, we introduce televisiting and telemedicine applications. In particular, we define modeling and simulation of hospital services as a base to investigate the role of technological innovations in order to explore the positive impact both on patient well-being as well as on business process management perspective.

This article proposes the analysis of the admissions to hospital-at-home service within the framework of process mining. In addition to conventional modeling in standard languages, relying on interviews and continuous improvement, we propose the adoption of an automatic process discovery technique based on data collected by the hospital information system. We focus on the patient admission process, in which staff discriminate cases of interest for the service. Our methodological framework starts with the extraction of process information from the existing dataset. Once obtained meaningful data for an event log analysis, we propose the adoption of a process discovery algorithm by using a specific tool for process mining. In the context of Business Process Management, we suggest a practical application to be explored in order to improve standard modeling, opening the way to perform business process simulation with scenario analysis.

This article focuses on the analysis of the admissions to hospital-at-home service within the framework of Business Process Management. While traditional process analysis deal with internal hospital services, having a particular and specific scenario, e.g. a ward of an hospital, here we investigate a quite innovative service with a strong socio-territorial impact based on real data. In particular, we are interested in the understanding of the selection process in which staff discriminate cases of interest for the service. We describe here our methodological framework combining data and event log analysis, modeling with standard language and business process simulation with scenario analysis.

This article focuses on the analysis of the admissions to hospital-at-home service within the framework of Business Process Management. While traditional process analysis deal with internal hospital services, having a particular and specific scenario, e.g. a ward of an hospital, here we investigate a quite innovative service with a strong socio-territorial impact based on real data. In particular, we are interested in the understanding of the selection process in which staff discriminate cases of interest for the service. We describe here our methodological framework combining data and event log analysis, modeling with standard language and business process simulation with scenario analysis.

This article focuses on e-Health solutions for monitoring patients with fragilities in the context of de-hospitalization. Assistive technologies clearly have a direct and positive impact on the quality of life of patients, but they also improve the overall management of the organizational processes. In the first steps of “La Casa nel Parco” project funded by Regione Piemonte, we introduce here a general Ambient Assisted Living Healthcare framework to include business process analysis, telehealth and telemedicine applications. In particular, we define modeling and simulation of hospital services as a base to investigate the role of technological innovations. The project further investigates the impact of a telehealth solution with a wrist-worn device, as well as a telemedicine application based on augmented reality applied to pharmaceutical products. Our framework allows to explore the positive impact both on patient well-being as well as on business process management perspective.

OBJECTIVE: Endometrial cancer (EC) is a complex gynecological neoplasm with several clinical, histopathological and genetic features. Different hormonal, metabolic and biochemical axes are involved in pathogenesis. Obesity is a well-known risk factor for this disease and the role of local and systemic effects of adipose tissue, especially in the promotion of subclinical chronic inflammation, is an important issue. Indeed. inflammation is related to the pathogenesis of different tumors, including EC. This review aims to remark the role of obesity and inflammation in the pathogenesis of EC cancer through an exploration of the current literature. MATERIALS AND METHODS: We performed a comprehensive review of the literature through a PubMed search using key words and including English language papers looking at this topic. RESULTS: Only few authors analyzed the role of inflammatory cytokines released by adipose tissue in visceral abdominal fat depots. Tumor Necrosis Factor-a, Interleukin-6, Interleukin-1 Receptor Antagonist, Nuclear Factor-kB, Leptin, Adiponectin and C Reactive Protein were studied for cancer risk prediction models, risk stratification or targeted therapies. Furthermore, genetic studies evaluated the effect of inflammatory cytokines secreted by visceral adipocytes in the modulation of angiogenesis and signaling pathways such as PI3K/AKT/mTOR, that result altered in the pathogenesis of EC. CONCLUSIONS: The identification of inflammatory biomarkers released by adipose tissue. in the pathogenesis of EC, could be useful in improving diagnostic accuracy, identifying targets of therapy, suggesting useful lifestyle behaviors. A deeper knowledge of the genetic background of alterations in inflammatory pathway genes could better define the population exposed to a higher susceptibility to EC due to genetic polymorphisms. Future studies are needed to better understand this field.

Introduction. This study was performed to evaluate if an early discharge to a Hospital at Home Service (HHS) produced differences in all cause mortality (combined endpoint) and hospital readmissions in elderly patients affected by severe heart failure.Methods. From September 1st 2008 to May 31st 2010, patients 65 years or older admitted to the Emergency Department of the San Giovanni Battista Hospital of Turin for acute decompensated CHF were selected. Patients were randomly assigned to two groups: 26 hospitalized in general wards where they received routine hospital care (group 1), 26 hospitalized and early discharged to the Hospital at Home Service- HHS (within 120 hours) (group 2).Results. Patients were very old (mean age 81 years), comorbid, 73% had severe symptoms of heart failure (NYHA IV), without differences between the two groups. No differences in mortality and hospital readmission rates were found. Mood level improved in HHS patients only.Discussion. This is a pilot study on early discharge for elderly patients with advanced heart failure. Home care should be considered as an important element toward adjusting the transition in frail hospitalized patients effectively.