Small Practices, Big (QI) Dreams: Customizing QI Efforts for Under-resourced Primary Care Practices to Improve Diabetes Disparities (Preprint)

2020 ◽  
Author(s):  
Sahnah Lim ◽  
Nadia S. Islam
Keyword(s):  
Primary Care ◽  
Clinical Practice ◽  
Electronic Health Record ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Low Income ◽  
Great Promise ◽  
Health Record ◽  
Small Practice ◽  
Electronic Health

UNSTRUCTURED Electronic health record quality improvement (QI) initiatives hold great promise in improving adoption of clinical practice guidelines, including those related to diabetes. QI initiatives implemented in under-resourced primary care settings that primarily serve racial/ethnic minority populations have potential to improve quality of care and ultimately improve diabetes disparities. The “Screen at 23” campaign was launched in 2011 to increase screening for prediabetes and diabetes at lower body mass index (BMI) thresholds (i.e., 23 kg/m2) for Asian Americans, in line with the new guidelines put forth by the American Diabetes Association. Here, we describe the implementation of a customized electronic health record QI initiative in under-resourced practices that primarily serve low-income South Asian populations in New York City, designed to increase diabetes screening using updated BMI guidelines and in alignment with the “Screen at 23” campaign. The customization involved the implementation of an innovative, semi-manual alternate solution to automated clinical decision support systems (CDSS) alerts in order to address the restrictions on customizing CDSS alerts in electronic health record platforms used in small practice settings. We also discuss challenges and strategies with this customized QI effort. Our experience suggests that multi-sector partnership engagement, user-centered approaches, and relationship-building with key stakeholders are even more critical in under-resourced, and small practice settings. Relatively simple technological solutions can be greatly beneficial in enhancing small practice capacity to engage in larger-scale QI initiatives. Tailored, context-driven approaches for implementation of equity-focused QI initiatives such as the one we describe can increase adoption of clinical practice guidelines, improve diabetes-related outcomes, and improve health disparities among under-served populations. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1186/s13063-019-3711-y

scholarly journals Identifying clinical features in primary care electronic health record studies: methods for codelist development

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e019637 ◽  
Author(s):  
Jessica Watson ◽  
Brian D Nicholson ◽  
Willie Hamilton ◽  
Sarah Price
Keyword(s):  
Primary Care ◽  
Sensitivity Analysis ◽  
Clinical Practice ◽  
Electronic Health Record ◽  
Clinical Features ◽  
Complete Agreement ◽  
Health Record ◽  
Shortness Of Breath ◽  
Statistical Software ◽  
Electronic Health

ObjectiveAnalysis 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.DesignWe 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.SettingThese 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.ParticipantsThe 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).ResultsOf 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.ConclusionsAlthough 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.

scholarly journals Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Electronic Health Record ◽  
Observational Studies ◽  
Meta Analysis ◽  
Data Sources ◽  
Health Record ◽  
Two Stage ◽  
One Stage ◽  
Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Identifying Factors Predicting Immunization Delay for Children Followed in an Urban Primary Care Network Using an Electronic Health Record

PEDIATRICS ◽  
2006 ◽  
Vol 118 (6) ◽  
pp. e1680-e1686 ◽  
Author(s):  
A. G. Fiks ◽  
E. A. Alessandrini ◽  
A. A. Luberti ◽  
S. Ostapenko ◽  
X. Zhang ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Health Record ◽  
Care Network ◽  
Electronic Health

scholarly journals Clinical practice guidelines and principles of care for people with dementia: a protocol for undertaking a Delphi technique to identify the recommendations relevant to primary care nurses in the delivery of person-centred dementia care

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e044843
Author(s):  
Caroline Gibson ◽  
Dianne Goeman ◽  
Mark William Yates ◽  
Dimity Pond
Keyword(s):  
Primary Care ◽  
General Practice ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Dementia Care ◽  
Care Nurse ◽  
Primary Care Nurse ◽  
People With Dementia ◽  
Primary Care Nurses

IntroductionNationally and internationally it is well recognised that dementia is poorly recognised and suboptimally managed in the primary care setting. There are multiple and complex reasons for this gap in care, including a lack of knowledge, high care demands and inadequate time for the general practitioner alone to manage dementia with its multiple physical, psychological and social dimensions. The primary care nurse potentially has a role in assisting the general practitioner in the provision of evidence-based dementia care. Although dementia-care guidelines for general practitioners exist, evidence on resources to support the primary care nurse in dementia care provision is scarce. The ‘Australian Clinical Practice Guidelines and Principles of Care for People with Dementia’ provides 109 recommendations for the diagnosis and management of dementia. This protocol describes a Delphi study to identify which of the 109 recommendations contained in these multidisciplinary guidelines are relevant to the primary care nurse in the delivery of person-centred dementia care in the general practice setting.Methods and analysisUsing a Delphi consensus online survey, an expert panel will grade each of the recommendations written in the ‘Clinical Practice Guidelines and Principles of Care for People with Dementia’ as high-to-low relevance with respect to the role of the primary care nurse in general practice. To optimise reliability of results, quality indicators will be used in the data collection and reporting of the study. Invited panel members will include Australian primary care nurses working in general practice, primary care nursing researchers and representatives of the Australian Primary Health Care Nurses Association, the peak professional body for nurses working in primary healthcare.Ethics and disseminationThis study has been approved by The University of Newcastle Human Research Ethics Committee (HREC) (H-2019-0029).Findings will be published in a peer-reviewed journal and presented at scientific conferences.

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