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A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record.

ABSTRACT: BACKGROUND:There is significant global policy interest related to enabling a data-driven approach for evidence-based primary care system development. This paper describes the development and initial testing of a prototype tool (the Problem-Oriented Primary Care System Development Record, or PCSDR) that enables a data-driven and contextualized approach to primary care system development. METHODS:The PCSDR is an electronic record that enables the systematic input, classification, structuring, storage, processing and analysis of different types of data related to the structure, function and performance of primary care systems over time. Data inputted into the PCSDR was coded using the WHO's PHC-IMPACT framework and classification system. The PCSDR's functionalities were tested by using a case study of primary care system development in Tajikistan. RESULTS:Tajikistan's case study demonstrated that the PCSDR is a potentially effective and conceptually-sound tool for the input, classification, structuring and storage of different data types from myriad sources. The PCSDR is therefore a basic data entry and data management system that enables query and analytics functions for health services research and evidence-based primary care system development functions. CONCLUSIONS:The PCSDR is a data system that enables a contextualized approach to evidence-based primary care system development. It represents a coherent and effective synthesis of the fields of primary care system development and performance assessment. The PCSDR enables analysts to leverage primary care performance assessment frameworks for a broad range of functions related to health systems analysis, improvement and the development of learning health systems.

SUBMITTER: Shukor AR

PROVIDER: S-EPMC7395390 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record.

Shukor Ali Rafik AR Barbazza Erica E Klazinga Niek N Kringos Dionne Sofia DS

BMC health services research 20200801 1

<h4>Background</h4>There is significant global policy interest related to enabling a data-driven approach for evidence-based primary care system development. This paper describes the development and initial testing of a prototype tool (the Problem-Oriented Primary Care System Development Record, or PCSDR) that enables a data-driven and contextualized approach to primary care system development.<h4>Methods</h4>The PCSDR is an electronic record that enables the systematic input, classification, st ...[more]

PMID: 32738904

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Project description:BackgroundSince the creation of the problem-oriented medical record, the building of problem lists has been the focus of many studies. To date, this issue is not well resolved, and building an appropriate contextualized problem list is still a challenge.ObjectiveThis paper aims to present the process of building a shared multipurpose common problem list at the Geneva University Hospitals. This list aims to bridge the gap between clinicians' language expressed in free text and secondary uses requiring structured information.MethodsWe focused on the needs of clinicians by building a list of uniquely identified expressions to support their daily activities. In the second stage, these expressions were connected to additional information to build a complex graph of information. A list of 45,946 expressions manually extracted from clinical documents was manually curated and encoded in multiple semantic dimensions, such as International Classification of Diseases, 10th revision; International Classification of Primary Care 2nd edition; Systematized Nomenclature of Medicine Clinical Terms; or dimensions dictated by specific usages, such as identifying expressions specific to a domain, a gender, or an intervention. The list was progressively deployed for clinicians with an iterative process of quality control, maintenance, and improvements, including the addition of new expressions or dimensions for specific needs. The problem management of the electronic health record allowed the measurement and correction of encoding based on real-world use.ResultsThe list was deployed in production in January 2017 and was regularly updated and deployed in new divisions of the hospital. Over 4 years, 684,102 problems were created using the list. The proportion of free-text entries decreased progressively from 37.47% (8321/22,206) in December 2017 to 18.38% (4547/24,738) in December 2020. In the last version of the list, over 14 dimensions were mapped to expressions, among which 5 were international classifications and 8 were other classifications for specific uses. The list became a central axis in the electronic health record, being used for many different purposes linked to care, such as surgical planning or emergency wards, or in research, for various predictions using machine learning techniques.ConclusionsThis study breaks with common approaches primarily by focusing on real clinicians' language when expressing patients' problems and secondarily by mapping whatever is required, including controlled vocabularies to answer specific needs. This approach improves the quality of the expression of patients' problems while allowing the building of as many structured dimensions as needed to convey semantics according to specific contexts. The method is shown to be scalable, sustainable, and efficient at hiding the complexity of semantics or the burden of constraint-structured problem list entry for clinicians. Ongoing work is analyzing the impact of this approach on how clinicians express patients' problems.

Project description:OBJECTIVE:Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. DESIGN:We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. SETTING:These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. PARTICIPANTS:The codelist was used to estimate the frequency of shortness of breath in a cohort of 28?216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). RESULTS:Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13?091 episodes of shortness of breath were identified in the cohort of 28?216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. CONCLUSIONS:Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Project description:ObjectiveTo develop and evaluate the psychometric properties of a scale measuring patient safety during the intrahospital transport process for intensive care.DesignThe scale was developed based on a theoretical model of the work system and patient safety, and items generated from participant observations. A Delphi study with international experts was used to establish content validity. Next, a cross-sectional study was undertaken to inform item reduction and evaluate construct validity and internal consistency.SettingThe questionnaire was distributed to healthcare practitioners at 12 intensive care units in Sweden.ParticipantsA total of 315 questionnaires were completed. Eligible participants were healthcare practitioners in the included units that performed an intrahospital transport during the study period. Inclusion criteria were (1) transports of patients within the hospital to undergo an examination or intervention, and (2) transports performed by staff from the intensive care unit. We excluded transports to a step-down unit or hospital ward.Outcome measuresPsychometric evaluation, including item analysis, validity and reliability testing.ResultsItems were reduced from 55 to 24, informed by distributional statistics, initial reliabilities, factor loadings and communalities. The final factor model consisted of five factors, accounting for 59% of variance. All items loaded significantly on only one factor (>0.35). The original conceptual model of teamwork, transport-related tasks, tools and technologies, environment, and organisation was maintained with regrouping of items. Cronbach's alpha ranged from 0.72 to 0.82 for each subscale (ie, factor).ConclusionsThe present study provides a self-report questionnaire to assess patient safety during intrahospital transport of patients in intensive care. The results indicate acceptable validity and reliability of the scale among a sample of Swedish healthcare practitioners. If further confirmatory testing supports the present results, this scale could be a useful tool to better understand safety prerequisites and improve clinical practice.

Dataset Information

A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record.

Publications

A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record.

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