Dataset Information

Using electronic patient records: defining learning outcomes for undergraduate education.

ABSTRACT:

Background

Healthcare professionals are required to access, interpret and generate patient data in the digital environment, and use this information to deliver and optimise patient care. Healthcare students are rarely exposed to the technology, or given the opportunity to use this during their training, which can impact on the digital competence of the graduating workforce. In this study we set out to develop and define domains of competence and associated learning outcomes needed by healthcare graduates to commence working in a digital healthcare environment.

Method

A National Working Group was established in the UK to integrate Electronic Patient Records (EPRs) into undergraduate education for healthcare students studying medicine, pharmacy, nursing and midwifery. The working group, comprising 12 academic institutions and representatives from NHS England, NHS Digital and EPR system providers, met to discuss and document key learning outcomes required for using EPRs in the healthcare environment. Outcomes were grouped into six key domains and refined by the group prior to external review by experts working in medical education or with EPRs.

Results

Six key domains of competence and associated learning outcomes were identified and defined. External expert review provided iterative refinement and amendment. The agreed domains were: 1) Digital Health: work as a practitioner in the digital healthcare environment; 2) Accessing Data: access and interpret patient data to inform clinical decision-making; 3) Communication: communicate effectively with healthcare professionals and patients in the digital environment; 4) Generating data: generate data for and about patients within the EPR; 5) Multidisciplinary working: work with healthcare professionals with and alongside EPRs; and 6) Monitoring and audit: monitor and improve the quality and safety of healthcare.

Conclusion

The six domains of competence and associated learning outcomes can be used by academics to guide the integration of EPRs into undergraduate healthcare programmes. This is key to ensuring that the future healthcare workforce can work with and alongside EPRs.

SUBMITTER: Pontefract SK

PROVIDER: S-EPMC6341543 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Using electronic patient records: defining learning outcomes for undergraduate education.

Pontefract S K SK Wilson K K

BMC medical education 20190122 1

<h4>Background</h4>Healthcare professionals are required to access, interpret and generate patient data in the digital environment, and use this information to deliver and optimise patient care. Healthcare students are rarely exposed to the technology, or given the opportunity to use this during their training, which can impact on the digital competence of the graduating workforce. In this study we set out to develop and define domains of competence and associated learning outcomes needed by hea ...[more]

PMID: 30670000

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Project description:IntroductionBy linking datasets, electronic records can be used to build large birth-cohorts, enabling researchers to cost-effectively answer questions relevant to populations over the life-course. Currently, around 5.8 million Palestinian refugees live in five settings: Jordan, Lebanon, Syria, West Bank, and Gaza Strip. The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) provides them with free primary health and elementary-school services. It maintains electronic records to do so.We aimed to establish a birth cohort of Palestinian refugees born between 1st January 2010 and 31st December 2020 living in five settings by linking mother obstetric records with child health and education records and to describe some of the cohort characteristics. In future, we plan to assess effects of size-at-birth on growth, health and educational attainment, among other questions.MethodsWe extracted all available data from 140 health centres and 702 schools across five settings, i.e. all UNRWA service users. Creating the cohort involved examining IDs and other data, preparing data, de-duplicating records, and identifying live-births, linking the mothers' and children's data using different deterministic linking algorithms, and understanding reasons for non-linkage.ResultsWe established a birth cohort of Palestinian refugees using electronic records of 972,743 live births. We found high levels of linkage to health records overall (83%), which improved over time (from 73% to 86%), and variations in linkage rates by setting: these averaged 93% in Gaza, 89% in Lebanon, 75% in Jordan, 73% in West Bank and 68% in Syria. Of the 423,580 children age-eligible to go to school, 47% went to UNRWA schools and comprised of 197,479 children with both health and education records, and 2,447 children with only education records. In addition to year and setting, other factors associated with non-linkage included mortality and having a non-refugee mother. Misclassification errors were minimal.ConclusionThis linked open birth-cohort is unique for refugees and the Arab region and forms the basis for many future studies, including to elucidate pathways for improved health and education in this vulnerable, understudied population. Our characterization of the cohort leads us to recommend using different sub-sets of the cohort depending on the research question and analytic purposes.

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Project description:BackgroundNowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.MethodsWe propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting.ResultsTo obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients.ConclusionOur results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

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Dataset Information

Using electronic patient records: defining learning outcomes for undergraduate education.

Background

Method

Results

Conclusion

Publications

Using electronic patient records: defining learning outcomes for undergraduate education.

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