Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda

Abstract

ObjectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.SettingA single, private, not-for profit hospital in rural, southwestern Uganda.ParticipantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the derivation dataset, and from 2015 to 2017, 10 068 visits produced 20 165 total chief complaints for the validation dataset.MethodsA retrospective review of an emergency centre quality assurance database was performed. Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen’s kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.ResultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.ConclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.

Emergency care status, priorities and standards for the Pacific region: A multiphase survey and consensus process across 17 different Pacific Island Countries and Territories

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“…7 The few efforts in this direction that do exist lack the imprimatur and validation standards of an international body. 8 Our group worked with colleagues from the World Health Organization (WHO) to create a pilot symptom list for use in low resource settings, representing an attempt by an international criterion-setting body in emergency care to standardize this type of collection of undifferentiated chief complaints in the emergency and acute care setting.…”

Defining High‐risk Emergency Chief Complaints: Data‐driven Triage for Low‐ and Middle‐income Countries

“…[10][11][12] To the authors' knowledge, a 2018 project within a Ugandan emergency unit was the first and only study to derive a unique CC ontology using locally captured data and a local lexicon. 13 This research reflects a larger trend in medicine, with increasing importance being placed on the role of ontologies to more precisely classify patients and support clinical reasoning. 14 Training programs in high-income countries (HICs) often focus on high-risk CCs, but definitions of riskiness tend to combine complex notions of patient safety, medical-legal landscapes, and challenging diagnoses.…”

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“…These studies have expanded the understanding of acute care presentations, but have been limited by use of preexisting or nonlocal lexicons, a focus on nonemergent cases, small patient populations, specific subsets of patients (i.e., trauma), and a lack of outcomes data 10‐12 . To the authors’ knowledge, a 2018 project within a Ugandan emergency unit was the first and only study to derive a unique CC ontology using locally captured data and a local lexicon 13 . This research reflects a larger trend in medicine, with increasing importance being placed on the role of ontologies to more precisely classify patients and support clinical reasoning 14 …”