Suitability of Electronic Health Record Data for Computational Phenotyping of Diabetes Mellitus at Nairobi Hospital, Nairobi City County, Kenya

This research aims to determine the applicability of routine healthcare in clinical informatics research. One of the key areas of research in precision medicine is computational phenotyping from longitudinal Electronic Health Record (EHR) data. The objective of this research was to determine how the interplay of EHR software design, the use of a data dictionary, the process of data collection, and the training and motivation of the human resource involved in the collection and entry of data into the EHR affect the quality of EHR data thus the suitability of such data for utility in computational phenotyping of diabetes mellitus. This research employed a prospective/retrospective study design at the diabetes clinic in Nairobi Hospital. The first source of data was from interviews with 32 staff; nurses, doctors, and health record officers using a referenced peer-reviewed usability questionnaire. Thereafter, a sample of EHR data collected during routine care between January 2012 and December 2016 was also analyzed by looking into the quality of clusters identified in the data using a density-based clustering algorithm and Statistical Package for Social Sciences (SPSS) version 21. Regression analysis shows that software design and the utility of a data dictionary explained 50.7% and 32.3% respectively in the improvement of the suitability of EHR data for computational phenotyping of diabetes mellitus. Also, EHR software was rated useful (82%) in accomplishing users’ daily tasks. However, EHR data were found to be unsuitable for utility in computational phenotyping of diabetes. Despite the fact that 88% of EHR data were clustered as noise, the clustering algorithm identified a total of 23 clusters from the diabetes dataset. However, with improved quality of EHR data, sub-phenotyping tasks would be achievable. This research concludes that the poor quality of EHR data is a result of employees’ unmet intrinsic factors of motivation.

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Desiderata for the development of next-generation electronic health record phenotype libraries

GigaScience ◽

10.1093/gigascience/giab059 ◽

2021 ◽

Vol 10 (9) ◽

Author(s):

Martin Chapman ◽

Shahzad Mumtaz ◽

Luke V Rasmussen ◽

Andreas Karwath ◽

Georgios V Gkoutos ◽

...

Keyword(s):

Best Practices ◽

Electronic Health Record ◽

Health Record ◽

Next Generation ◽

Electronic Health Record Data ◽

High Quality ◽

Record Data ◽

Electronic Health ◽

Further Development

Abstract Background High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. Methods A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.

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