New National Healthcare Information System

2006 ◽  
Vol 44 (6) ◽  
Author(s):  
Walter Bergamaschi ◽  
Laura Rapone ◽  
Elianna Sorda
Keyword(s):  
Information System ◽  
Strategic Planning ◽  
Health Information ◽  
Health Information System ◽  
Quality Of Services ◽  
Comparable Data ◽  
National Healthcare ◽  
Healthcare Needs ◽  
Healthcare Information System

AbstractCurrently the increased healthcare needs of the population call for strategic planning that provides tools to balance the costs of the system with the quality of services offered. The first requirement for this end is reliable, timely and comparable data. Therefore, a New Health Information System (NSIS) is needed that will contribute to achieving this goal.

Quality of Routine Health Information System Data and Associated Factors Among Departments in Public Health Facilities of Harari Region, Ethiopia

2021 ◽  
Author(s):  
Adisu Tafari Shama ◽  
Hirbo Shore Roba ◽  
Admas Abera ◽  
Negga Baraki
Keyword(s):  
Public Health ◽  
Information System ◽  
Data Quality ◽  
Health Information ◽  
Health Information System ◽  
Health Facilities ◽  
Quality Data ◽  
Public Health Facilities ◽  
System Data

Abstract Background: Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.Methods: A cross-sectional study was conducted in all public health facilities in Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation check-lists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P-value <0.05. Result: The study found a good quality data in 51.35% (95% CI, 44.6-58.1) of the departments in public health facilities in Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to departments found in the health posts. The presence of trained staffs able to fill reporting formats (AOR=2.474; 95%CI: 1.124-5.445) and provision of feedback (AOR=3.083; 95%CI: 1.549-6.135) were also significantly associated with data quality. Conclusion: The level of good data quality in the public health facilities was less than the expected national level. Training should be provided to increase the knowledge and skills of the health workers.

scholarly journals Evaluasi Sistem Informasi Manajemen Puskesmas Guna Mendukung Penerapan Sikda Generik Menggunakan Metode Hot Fit Di Kabupaten Purworejo

2016 ◽  
Vol 4 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Viera Juniver Thenu ◽  
Eko Sediyono ◽  
Cahya Tri Purnami
Keyword(s):  
Information System ◽  
Health Center ◽  
Health Information ◽  
Health Information System ◽  
Health Centers ◽  
Accurate Information ◽  
System Quality ◽  
District Health ◽  
Routine Monitoring

Purworejo District Health Office (DHO) has developed Health Center Management Information System (HCMIS) to provide quick, precise, and accurate information for supporting the process of decision making and the policy of services at Health Center. However, there was still any empty forms and did not use the system. To standardize Health Information System, Ministry of Health has released a concept of Generic SIKDA. Therefore, the system applied by Purworejo DHO needs to be evaluated using the method of HOT fit. This was qualitative research using indepth interview and direct observation. Main informants were nine data operators and informants for triangulation purpose was six persons from health centers and five persons from DHO. Furthermore, data were analyzed using content analysis. The result of this research revealed that the application of HCMIS at Purworejo DHO was equal to Generic SIKDA. The system had been applied routinely but there was no a guidance book and training. Number of human resources was sufficient. However, there needed to make an implementer team of Health Information System in accordance with competency. There was no routine monitoring and current budget was not sufficient to maintain hardware to support the system. Quality of the system used was good, easy to use, and available of menu to communicate data. Unfortunately, quality of resulted information had still been inaccurate and incomplete because it did not cover data of services at Subsidiary Health Centers (SHC) and Village Health Posts (VHP). The lateness of services was related to a funding procedure. HCMIS at Purworejo DHO was equal to Generic SIKDA but it had not been used to make a decision. As a suggestion, DHO needs to conduct training, monitor, and provide hardware and a network. Meanwhile, Health Centers needs to make a commitment for applying the HCMIS maximally by SHC and VHP.

scholarly journals Quality of the Mental Health Information System in a Specialized Mental Hospital in Bangladesh

2018 ◽  
Vol 26 (2) ◽  
pp. 180
Author(s):  
Nazmun Nuri ◽  
Malabika Sarker ◽  
Helal Ahmed ◽  
Mohammad Hossain ◽  
Fekri Dureab ◽  
...  
Keyword(s):  
Mental Health ◽  
Information System ◽  
Health Information ◽  
Mental Hospital ◽  
Health Information System ◽  
Mental Health Information ◽  
Mental Health Information System

Development of a Computerized Information System in the Harare City Health Department

1995 ◽  
Vol 34 (03) ◽  
pp. 297-301 ◽  
Author(s):  
G. B. Woelk ◽  
I. M. Moyo
Keyword(s):  
Information System ◽  
Health Information ◽  
Health Information System ◽  
Implementation Process ◽  
Health Department ◽  
Original Data ◽  
Quality Of Information ◽  
Careful Management ◽  
Harare City

Abstract:The experience is reported with the health information system of the Health Department of Harare City, five years after its introduction. The system was evaluated in terms of the implementation procedure and improvements in the management process. It is concluded that careful management of the implementation process itself is a key to the successful realization of a health information system in developing countries. The statement that the quality of information produced during a computerization process is only as good as the original data fed into it, is emphasized.

scholarly journals First study of the RuPatient health information system with optical character recognition of medical records based on machine learning

2022 ◽  
Vol 20 (8) ◽  
pp. 3080
Author(s):  
A. A. Komkov ◽  
V. P. Mazaev ◽  
S. V. Ryazanova ◽  
D. N. Samochatov ◽  
E. V. Koshkina ◽  
...  
Keyword(s):  
Information System ◽  
Health Information ◽  
Medical Records ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Health Information System ◽  
Document Management ◽  
Medical Documentation ◽  
Optical Character

RuPatient health information system (HIS) is a computer program consisting of a doctor-patient web user interface, which includes algorithms for recognizing medical record text and entering it into the corresponding fields of the system.Aim. To evaluate the effectiveness of RuPatient HIS in actual clinical practice.Material and methods. The study involved 10 cardiologists and intensivists of the department of cardiology and сardiovascular intensive care unit of the L. A. Vorokhobov City Clinical Hospital 67 We analyzed images (scanned copies, photos) of discharge reports from patients admitted to the relevant departments in 2021. The following fields of medical documentation was recognized: Name, Complaints, Anamnesis of life and illness, Examination, Recommendations. The correctness and accuracy of recognition of entered information were analyzed. We compared the recognition quality of RuPatient HIS and a popular optical character recognition application (FineReader for Mac).Results. The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). Data that was not included in the category was also recognized and entered in the comments field. The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively.Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.

Engineering Dependable Health Information Systems

2005 ◽  
pp. 91-109 ◽  
Author(s):  
Khin Than Win ◽  
Peter Croll
Keyword(s):  
Information System ◽  
Knowledge Management ◽  
Information Systems ◽  
Electronic Health Record ◽  
Health Information ◽  
Health Information System ◽  
Health Information Systems ◽  
Health Record ◽  
The Relationship

Effective and appropriate implementation of health information systems assists with an organization’s knowledge management. To enhance a user’s trustworthiness and full adoption, a health information system needs to be dependable. This chapter reviews the different development methodologies available for engineering dependable solutions and their application by citing two case studies as an example. Health information systems cover a diverse set of applications. The focus in this chapter is on the development of electronic health record systems, the importance of dependability, and the relationship between dependability and data quality of the health record systems.

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