Desarrollo e implementación de un modelo de datos de prestaciones médicas para un hospital

En el contexto de la digitalización de la actividad asistencial del Hospital Alemán se desarrollan diferentes herramientas para el manejo del sistema hospitalario. Una de las herramientas clave es la descripción exhaustiva de las actividades que se desarrollan en el Hospital Alemán para ser explotada tanto desde el punto de vista de facturación como para el análisis epidemiológico del comportamiento de las poblaciones involucradas, y también para simplificar el pedido y prefacturación de las mismas. El objetivo de este trabajo es describir el modelo de datos utilizado para conceptualizar las actividades asociadas a la salud y la implementación de dicho modelo en nuestro hospital. Se describe el escenario inicial y el desarrollo del modelo de datos. La utilización de ontologías en el proceso de definición de las actividades médicas nos permitió trabajar con un sistema autodefinido, flexible y sobre todo, computable. Nos permite manejar dinámicamente las necesidades de los usuarios y al mismo tiempo limitarlos a trabajar en un ambiente controlado.Sociedad Argentina de Informática e Investigación Operativ

Predicting prescription patterns

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references (leaves 43-49).Electronic prescription software is replacing traditional handwritten medication orders. This development however doesn't come without a cost and speed has been one of the most complained about issues. It is important to address this problem and develop methods to reduce the time spent entering medication orders into computerized prescription software. The objective of this study was to understand the structure of prescription patterns and explore the possibility of designing a method that will predict prescription patterns with only the knowledge of past prescription history. Various machine-learning methods were used and their performance measured by the accuracy of prediction as well as their ability to produce desirable results, within practical time limits. This paper presents a method to transform prescription data into a stochastic time series for prediction. The paper also presents a new nonlinear local algorithm based on nearest neighbor search. In analyzing the database the drug patterns were found to be diverse and over 30% of the patients were unique, in the sense that no other patient had been prescribed the same set of active ingredients. In spite of this diversity, it was possible to create a list of 20 drugs that contained the drug to be prescribed next for 70.2% of patients. This suggests that probabilistically created pick lists, tailored specifically for one patient at the time of prescription, might be used to ease the prescription process. However, further research is needed to evaluate the impact of such lists on prescription habits.by Ívar S. Helgason.S.M

Physicians' and Nurses' Opinions about the Impact of a Computerized Provider Order Entry System on Their Workflow

INTRODUCTION: In clinical practices, the use of information technology, especially computerized provider order entry (CPOE) systems, has been found to be an effective strategy to improve patient care. This study aimed to compare physicians' and nurses' views about the impact of CPOE on their workflow. METHODS: This case study was conducted in 2012. The potential participants included all physicians (n = 28) and nurses (n = 145) who worked in a teaching hospital. Data were collected using a five-point Likert-scale questionnaire and were analyzed using SPSS version 18.0. RESULTS: The results showed a significant difference between physicians' and nurses' views about the impact of the system on interorganizational workflow (p = .001) and working relationships between physicians and nurses (p = .017). CONCLUSION: Interorganizational workflow and working relationships between care providers are important issues that require more attention. Before a CPOE system is designed, it is necessary to identify workflow patterns and hidden structures to avoid compromising quality of care and patient safety

Acceptance Behavior of Electronic Prescription System

Electronic prescription is one of information systems to support medical services based on computer technology to support clinical decision (Clinical Decision Support, CDS). Studies proved that the use of this system can decrease medication error. One of the hospitals that have applied this system is Meuraxa Hospital. This study aimed to determine the acceptance behavior of electronic prescription information systems. The respondents of this study were 30 doctors at specialist clinic. Data was collected by distributing questionnaires on August 1st-23rd, 2016. Data was processed using SmartPLS 3.0. Results showed that there was a positive effect of effort expectancy factor on behavioral intention and facilitating factors on the use behavior towards receipt of electronic prescription. It is expected that the management of Meuraxa hospital develop an existing electronic prescription system by accommodating the needs of the system users

CPOE in Iran-A viable prospect?. Physicians' opinions on using CPOE in an Iranian teaching hospital

Background: In recent years, the theory that on-line clinical decision support systems can improve patients' safety among hospitalised individuals has gained greater acceptance. However, the feasibility of implementing such a system in a middle or low-income country has rarely been studied. Understanding the current prescription process and a proper needs assessment of prescribers can act as the key to successful implementation. Objectives: The aim of this study was to explore physicians' opinions on the current prescription process, and the expected benefits and perceived obstacles to employ Computerised Physician Order Entry in an Iranian teaching hospital. Methods: Initially, the interview guideline was developed through focus group discussions with eight experts. Then semi-structured interviews were held with 19 prescribers. After verbatim transcription, inductive thematic analysis was performed on empirical data. Forty hours of on-looker observations were performed in different wards to explore the current prescription process. Results: The current prescription process was identified as a physician-centred, top-down, model, where prescribers were found to mostly rely on their memories as well as being overconfident. Some errors may occur during different paper-based registrations, transcriptions and transfers. Physician opinions on Computerised Physician Order Entry were categorised into expected benefits and perceived obstacles. Confidentiality issues, reduction of medication errors and educational benefits were identified as three themes in the expected benefits category. High cost, social and cultural barriers, data entry time and problems with technical support emerged as four themes in the perceived obstacles category. Conclusions: The current prescription process has a high possibility of medication errors. Although there are different barriers confronting the implementation and continuation of Computerised Physician Order Entry in Iranian hospitals, physicians have a willingness to use them if these systems provide significant benefits. A pilot study in a limited setting and a comprehensive analysis of health outcomes and economic indicators should be performed, to assess the merits of introducing Computerised Physician Order Entry with decision support capabilities in Iran. © 2008 Elsevier Ireland Ltd. All rights reserved

Clinical review: Medication errors in critical care

Medication errors in critical care are frequent, serious, and predictable. Critically ill patients are prescribed twice as many medications as patients outside of the intensive care unit (ICU) and nearly all will suffer a potentially life-threatening error at some point during their stay. The aim of this article is to provide a basic review of medication errors in the ICU, identify risk factors for medication errors, and suggest strategies to prevent errors and manage their consequences