The implementation of electronic health records: A case study of bush computing the Ngaanyatjarra Lands

The challenges of implementing electronic health records (EHRs) have received some attention, but little is known about the process of transitioning from paper-based to electronic files. In this paper, a mixed approach using combined qualitative and quantitative methods is utilized. The authors enrolled nine administrative managers and 87 employees from different backgrounds, all part of a regional hospital at Flacq in Mauritius, from April to May 2011. Employees responded to a survey on various aspects pertaining to their eagerness to accept the shift to electronic health records and their views on the probability of success. Descriptive and inferential statistics were used to analyze the quantitative results and content analysis was performed on the qualitative data. Nurses performing at middle level agreed that a shift to EHR is positive but felt that it might take a long time to effect the change. With its implementation, they agreed that advantages like up to date information, diminished workload, and cost effectiveness would be easily attained. In contrast, focus groups confirmed that without the collaboration and support of management, implementation of EHR would prove arduous.

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Improving the Use of Big Data Analytics within Electronic Health Records: A Case Study based OpenEHR

Procedia Computer Science ◽

10.1016/j.procs.2018.01.098 ◽

2018 ◽

Vol 127 ◽

pp. 60-68 ◽

Cited By ~ 10

Author(s):

Fadoua Khennou ◽

Youness Idrissi Khamlichi ◽

Nour El Houda Chaoui

Keyword(s):

Big Data ◽

Electronic Health Records ◽

Data Analytics ◽

Big Data Analytics ◽

Health Records ◽

Electronic Health

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Same, same but different: Perceptions of patients’ online access to electronic health records among healthcare professionals

Health Informatics Journal ◽

10.1177/1460458218779101 ◽

2018 ◽

Vol 25 (4) ◽

pp. 1538-1548 ◽

Cited By ~ 5

Author(s):

Sofie Wass ◽

Vivian Vimarlund

Keyword(s):

Primary Care ◽

Electronic Health Records ◽

Healthcare Professionals ◽

Outpatient Clinics ◽

Health Records ◽

Documented Information ◽

Information Professionals ◽

Online Access ◽

Electronic Health

In this study, we explore how healthcare professionals in primary care and outpatient clinics perceive the outcomes of giving patients online access to their electronic health records. The study was carried out as a case study and included a workshop, six interviews and a survey that was answered by 146 healthcare professionals. The results indicate that professionals working in primary care perceive that an increase in information-sharing with patients can increase adherence, clarify important information to the patient and allow the patient to quality-control documented information. Professionals at outpatient clinics seem less convinced about the benefits of patient accessible electronic health records and have concerns about how patients manage the information that they are given access to. However, the patient accessible electronic health record has not led to a change in documentation procedures among the majority of the professionals. While the findings can be connected to the context of outpatient clinics and primary care units, other contextual factors might influence the results and more in-depth studies are therefore needed to clarify the concerns.

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Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care

Health Informatics Journal ◽

10.1177/1460458217733288 ◽

2017 ◽

Vol 25 (3) ◽

pp. 951-959 ◽

Cited By ~ 4

Author(s):

Gregor Stiglic ◽

Primoz Kocbek ◽

Nino Fijacko ◽

Aziz Sheikh ◽

Majda Pajnkihar

Keyword(s):

Missing Data ◽

Electronic Health Records ◽

Prediction Model ◽

Electronic Health Record ◽

Health Record ◽

Electronic Health Record Data ◽

Health Records ◽

Record Data ◽

Electronic Health

The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

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