Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Lewis Score - Prognostic value in patients with isolated small bowel Crohn's disease

Background and aims: Small bowel capsule endoscopy (SBCE) allows mapping of small bowel inflammation in Crohn’s disease (CD). We aimed to assess the prognostic value of the severity of inflammatory lesions, quantified by the Lewis score (LS), in patients with isolated small bowel CD. Methods: A retrospective study was performed in which 53 patients with isolated small bowel CD were submitted to SBCE at the time of diagnosis. The Lewis score was calculated and patients had at least 12 months of follow-up after diagnosis. As adverse events we defined disease flare requiring systemic corticosteroid therapy, hospitalization and/or surgery during follow-up. We compared the incidence of adverse events in 2 patient subgroups, i.e. those with moderate or severe inflammatory activity (LS =790) and those with mild inflammatory activity (135 = LS < 790). Results: The LS was =790 in 22 patients (41.5%), while 58.5% presented with LS between 135 and 790. Patients with a higher LS were more frequently smokers (p = 0.01), males (p = 0017) and under immunosuppressive therapy (p = 0.004). In multivariate analysis, moderate to severe disease at SBCE was independently associated with corticosteroid therapy during follow-up, with a relative risk (RR) of 5 (p = 0.011; 95% confidence interval [CI] 1.5–17.8), and for hospitalization, with an RR of 13.7 (p = 0 .028; 95% CI 1.3–141.9). Conclusion: In patients with moderate to severe inflammatory activity there were higher prevalences of corticosteroid therapy demand and hospitalization during follow-up. Thus, stratifying the degree of small bowel inflammatory activity with SBCE and LS calculation at the time of diagnosis provided relevant prognostic value in patients with isolated small bowel CD.

Plataforma de apoio à decisão nos cuidados de ginecologia e obstetrícia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

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.

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.