A Software Architecture-Based Framework Supporting Suggestion of Medical Surveillance Level from Classification of Electronic Patient Records

2009 ◽  
Author(s):  
Juliana T. Pollettini ◽  
Flávia P. Nicolas ◽  
Sylvia G. Panico ◽  
Julio C. Daneluzzi ◽  
Renato Tinos ◽  
...  
Keyword(s):  
Software Architecture ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Medical Surveillance

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

2012 ◽  
Vol 36 (6) ◽  
pp. 3861-3874 ◽  
Author(s):  
Juliana T. Pollettini ◽  
Sylvia R. G. Panico ◽  
Julio C. Daneluzzi ◽  
Renato Tinós ◽  
José A. Baranauskas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Electronic Patient Records in Medical Practice: A Multidisciplinary Endeavor

1999 ◽  
Vol 38 (04/05) ◽  
pp. 287-288 ◽  
Author(s):  
J. van der Lei ◽  
P. W. Moorman ◽  
M. A. Musen
Keyword(s):  
Medical Practice ◽  
Patient Records ◽  
Electronic Patient Records

Postmarketing Surveillance Based on Electronic Patient Records: The IPCI Project

1999 ◽  
Vol 38 (04/05) ◽  
pp. 339-344 ◽  
Author(s):  
J. van der Lei ◽  
B. M. Th. Mosseveld ◽  
M. A. M. van Wijk ◽  
P. D. van der Linden ◽  
M. C. J. M. Sturkenboom ◽  
...  
Keyword(s):  
General Practitioner ◽  
General Practitioners ◽  
Validation Studies ◽  
Resource Planning ◽  
Routine Practice ◽  
Patient Records ◽  
Postmarketing Surveillance ◽  
Electronic Patient Records ◽  
Use Of Data ◽  
Data Requirements

AbstractResearchers claim that data in electronic patient records can be used for a variety of purposes including individual patient care, management, and resource planning for scientific research. Our objective in the project Integrated Primary Care Information (IPCI) was to assess whether the electronic patient records of Dutch general practitioners contain sufficient data to perform studies in the area of postmarketing surveillance studies. We determined the data requirements for postmarketing surveil-lance studies, implemented additional software in the electronic patient records of the general practitioner, developed an organization to monitor the use of data, and performed validation studies to test the quality of the data. Analysis of the data requirements showed that additional software had to be installed to collect data that is not recorded in routine practice. To avoid having to obtain informed consent from each enrolled patient, we developed IPCI as a semianonymous system: both patients and participating general practitioners are anonymous for the researchers. Under specific circumstances, the researcher can contact indirectly (through a trusted third party) the physician that made the data available. Only the treating general practitioner is able to decode the identity of his patients. A Board of Supervisors predominantly consisting of participating general practitioners monitors the use of data. Validation studies show the data can be used for postmarketing surveillance. With additional software to collect data not normally recorded in routine practice, data from electronic patient record of general practitioners can be used for postmarketing surveillance.

scholarly journals Electronic patient record and its effects on social aspects of interprofessional collaboration and clinical workflows in hospitals (eCoCo): a mixed methods study protocol

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Marina Beckmann ◽  
Kerstin Dittmer ◽  
Julia Jaschke ◽  
Ute Karbach ◽  
Juliane Köberlein-Neu ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Health Services ◽  
Healthcare Professionals ◽  
Social Research ◽  
Interprofessional Collaboration ◽  
Participant Observation ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Daily Work ◽  
Network Analyses

Abstract Background The need for and usage of electronic patient records within hospitals has steadily increased over the last decade for economic reasons as well as the proceeding digitalization. While there are numerous benefits from this system, the potential risks of using electronic patient records for hospitals, patients and healthcare professionals must also be discussed. There is a lack in research, particularly regarding effects on healthcare professionals and their daily work in health services. The study eCoCo aims to gain insight into changes in interprofessional collaboration and clinical workflows resulting from introducing electronic patient records. Methods eCoCo is a multi-center case study integrating mixed methods from qualitative and quantitative social research. The case studies include three hospitals that undergo the process of introducing electronic patient records. Data are collected before and after the introduction of electronic patient records using participant observation, interviews, focus groups, time measurement, patient and employee questionnaires and a questionnaire to measure the level of digitalization. Furthermore, documents (patient records) as well as structural and administrative data are gathered. To analyze the interprofessional collaboration qualitative network analyses, reconstructive-hermeneutic analyses and document analyses are conducted. The workflow analyses, patient and employee assessment analyses and classification within the clinical adoption meta-model are conducted to provide insights into clinical workflows. Discussion This study will be the first to investigate the effects of introducing electronic patient records on interprofessional collaboration and clinical workflows from the perspective of healthcare professionals. Thereby, it will consider patients’ safety, legal and ethical concerns and quality of care. The results will help to understand the organization and thereby improve the performance of health services working with electronic patient records. Trial registration The study was registered at the German clinical trials register (DRKS00023343, Pre-Results) on November 17, 2020.

scholarly journals Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sophie Relph ◽  
◽  
Maria Elstad ◽  
Bolaji Coker ◽  
Matias C. Vieira ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Quality ◽  
Data Management ◽  
Randomised Control Trial ◽  
Data Dictionary ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Cluster Randomised ◽  
Quality Checks ◽  
Data Harmonisation

Abstract Background The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. Methods The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Results Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1–4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0–1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conclusions Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. Trial registration Primary registry and trial identifying number: ISRCTN 67698474. Registered on 02/11/16.

Sign in / Sign up

Export Citation Format

Share Document