Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Epidémiologie clinique: définitions, utilités et raisons pour un développement en Suisse. [Clinical epidemiology: definitions, uses and reasons for development in Switzerland].

Clinical epidemiology is the most currently used name for a comparatively new branch of medicine covering a certain number of activities related to the practice of clinical medicine, but using epidemiological techniques and methods. Clinical epidemiology has only just begun to be known in Europe, whereas units are being increasingly developed and expanded in North America, particularly within the clinical departments of hospitals. The methods it offers are valid for both practicing physicians and hospital doctors (or those being trained in hospitals) and serve the purpose of promoting a better quality medical service, especially where a more adequate evaluation of the effectiveness of diagnostic methods, therapy and prognosis in medicine is concerned. Clinical epidemiology proposes a methodology of medical reasoning and of decision-making, as well as techniques intended to facilitate the indispensable task of keeping up with advances in medical knowledge.

Simulación de departamentos de urgencias empleando un modelo basado en agentes

Los servicios de salud son sistemas muy complejos, pero de alta importancia, especialmente en algunos momentos críticos, en todo el mundo. Los departamentos de urgencias pueden ser una de las áreas más dinámicas y cambiables de todos los servicios de salud y a la vez más vulnerables a dichos cambios. La mejora de esos departamentos se puede considerar uno de los grandes retos que tiene cualquier administrador de un hospital, y la simulación provee una manera de examinar este sistema tan complejo sin poner en peligro los pacientes que son atendidos. El objetivo de este trabajo ha sido el modelado de un departamento de urgencias y el desarrollo de un simulador que implementa este modelo con la finalidad de explorar el comportamiento y las características de dicho servicio de urgencias. El uso del simulador ofrece la posibilidad de visualizar el comportamiento del modelo con diferentes parámetros y servirá como núcleo de un sistema de ayuda a la toma de decisiones que pueda ser usado en departamentos de urgencias. El modelo se ha desarrollado con técnicas de modelado basado en agentes (ABM) que permiten crear modelos funcionalmente más próximos a la realidad que los modelos de colas o de dinámicas de sistemas, al permitir la inclusión de la singularidad que implica el modelado a nivel de las personas. Los agentes del modelo presentado, descritos internamente como máquinas de estados, representan a todo el personal del departamento de urgencias y los pacientes que usan este servicio. Un análisis del modelo a través de su implementación en el simulador muestra que el sistema se comporta de manera semejante a un departamento de urgencias real.

Proyecto de implantación de un Sistema de BI para el análisis de la calidad de un Servicio.

Proyecto de implantación de un sistema de business intelligence para el análisis de la calidad de un servicio. El objetivo del presente proyecto es el análisis de una herramienta de apoyo al estudio de la calidad de un servicio, en este caso, servicio sanitario, enfocada desde la perspectiva del área de Ingeniería del software.

Improving appropriateness of antibiotic therapy: randomized trial of an intervention to foster reassessment of prescription after 3 days.

OBJECTIVES: Reassessment of ongoing antibiotic therapy is an important step towards appropriate use of antibiotics. This study was conducted to evaluate the impact of a short questionnaire designed to encourage reassessment of intravenous antibiotic therapy after 3 days. PATIENTS AND METHODS: Patients hospitalized on the surgical and medical wards of a university hospital and treated with an intravenous antibiotic for 3-4 days were randomly allocated to either an intervention or control group. The intervention consisted of mailing to the physician in charge of the patient a three-item questionnaire referring to possible adaptation of the antibiotic therapy. The primary outcome was the time elapsed from randomization until a first modification of the initial intravenous antibiotic therapy. It was compared within both groups using Cox proportional-hazard modelling. RESULTS: One hundred and twenty-six eligible patients were randomized in the intervention group and 125 in the control group. Time to modification of intravenous antibiotic therapy was 14% shorter in the intervention group (adjusted hazard ratio for modification 1.28, 95% CI 0.99-1.67, P = 0.06). It was significantly shorter in the intervention group compared with a similar group of 151 patients observed during a 2 month period preceding the study (adjusted hazard ratio 1.17, 95% CI 1.03-1.32, P = 0.02). CONCLUSION: The results suggest that a short questionnaire, easily adaptable to automatization, has the potential to foster reassessment of antibiotic therapy.

Construcción de un SIG para la Gestión de Explotación de Recursos del Subsuelo

Aquest Treball final de carrera (TFC) és una implantació d'una solució SIG, amb l¿objectiu d¿ajudar a prendre decisions en l'explotació de recursos del subsòl, dit d'una altra manera, una eina de suport per valorar quines són les parcel·les del terreny òptimes per iniciar la seva explotació. El Treball es divideix en dues parts, una teòrica i una altra pràctica. La part teòrica consisteix en l'estudi de les característiques fonamentals d'un SIG, a més d'un estudi pràctic dels aspectes generals del programa gvSIG. La part pràctica consisteix en el desenvolupament d'una solució SIG amb la finalitat de donar resposta als objectius proposats en aquest Treball final de carrera.

Análisis de contenido de la lista dedistribución IweTel (1998-2000)

La llista de distribució s'ha mostrat durant l'última dècada com una eina eficaç a l'hora de vertebrar la comunicació professional. De fet, és un primer pas cap a les comunitats virtuals. En aquest article s'estudia l'evolució d'un cas concret, IweTel, i de l'ús que els subscriptors van fer -ne, a partir de l'anàlisi de contingut dels missatges que van ser enviats entre 1998 i 2000. De l'estudi es desprèn que es tracta d'una llista utilitzada fonamentalment per a la comunicació professional i l'intercanvi d'informació, encara que pateix d'una falta notòria d'implicació activa per part dels seus subscriptors. Per altra banda, encara que han estat notables les millores fruit de la moderació, hi ha una absència viciosa de contingut científic o acadèmic en els debats desenvolupats.

A prediction rule to identify low-risk patients with pulmonary embolism.

BACKGROUND: A simple prognostic model could help identify patients with pulmonary embolism who are at low risk of death and are candidates for outpatient treatment. METHODS: We randomly allocated 15,531 retrospectively identified inpatients who had a discharge diagnosis of pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our rule to predict 30-day mortality using classification tree analysis and patient data routinely available at initial examination as potential predictor variables. We used data from a European prospective study to externally validate the rule among 221 inpatients with pulmonary embolism. We determined mortality and nonfatal adverse medical outcomes across derivation and validation samples. RESULTS: Our final model consisted of 10 patient factors (age > or = 70 years; history of cancer, heart failure, chronic lung disease, chronic renal disease, and cerebrovascular disease; and clinical variables of pulse rate > or = 110 beats/min, systolic blood pressure < 100 mm Hg, altered mental status, and arterial oxygen saturation < 90%). Patients with none of these factors were defined as low risk. The 30-day mortality rates for low-risk patients were 0.6%, 1.5%, and 0% in the derivation, internal validation, and external validation samples, respectively. The rates of nonfatal adverse medical outcomes were less than 1% among low-risk patients across all study samples. CONCLUSIONS: This simple prediction rule accurately identifies patients with pulmonary embolism who are at low risk of short-term mortality and other adverse medical outcomes. Prospective validation of this rule is important before its implementation as a decision aid for outpatient treatment.

Influencia de la temperatura en la ingesta de pacientes hospitalizados.

BACKGROUND AND OBJECTIVES Prevalence of hyponutrition in hospitalized patients is very high and it has been shown to be an important prognostic factor. Most of admitted patients depend on hospital food to cover their nutritional demands being important to assess the factors influencing their intake, which may be modified in order to improve it and prevent the consequences of inadequate feeding. In previous works, it has been shown that one of the worst scored characteristics of dishes was the temperature. The aim of this study was to assess the influence of temperature on patient's satisfaction and amount eaten depending on whether the food was served in isothermal trolleys keeping proper food temperature or not. MATERIAL AND METHODS We carried out satisfaction surveys to hospitalized patients having regular diets, served with or without isothermal trolleys. The following data were gathered: age, gender, weight, number of visits, mobility, autonomy, amount of orally taken medication, intake of out-of-hospital foods, qualification of food temperature, presentation and smokiness, amount of food eaten, and reasons for not eating all the content of the tray. RESULTS Of the 363 surveys, 134 (37.96%) were done to patients with isothermal trays and 229 (62.04%) to patients without them. Sixty percent of the patients referred having eaten less than the normal amount within the last week, the most frequent reason being decreased appetite. During lunch and dinner, 69.3% and 67.7%, respectively, ate half or less of the tray content, the main reasons being as follows: lack of appetite (42% at lunch time and 40% at dinner), do not like the food (24.3 and 26.2%) or taste (15.3 and 16.8%). Other less common reasons were the odor, the amount of food, having nausea or vomiting, fatigue, and lack of autonomy. There were no significant differences in the amount eaten by gender, weight, number of visits, amount of medication, and level of physical activity. The food temperature was classified as adequate by 62% of the patients, the presentation by 95%, and smokiness by 85%. When comparing the patients served with or without isothermal trays, there were no differences with regards to baseline characteristics analyzed that might have had an influence on amount eaten. Ninety percent of the patients with isothermal trolley rated the food temperature as good, as compared with 57.2% of the patients with conventional trolley, the difference being statistically significant (P = 0.000). Besides, there were differences in the amount of food eaten between patients with and without isothermal trolley, so that 41% and 27.7% ate all the tray content, respectively, difference being statistically significant (P = 0.007). There were no differences in smokiness or presentation rating. CONCLUSIONS Most of the patients (60%) had decreased appetite during hospital admission. The percentage of hospitalized patients rating the food temperature as being good is higher among patients served with isothermal trolleys. The amount of food eaten by the patients served with isothermal trolleys is significantly higher that in those without them.