Factors affecting clinicians' adherence to principles of diagnosis documentation: A concept mapping approach for improved decision-making

Patient Insights into the Diagnosis of Smell and Taste Disorders in the United States

OBJECTIVES

Diagnosis of smell/taste dysfunction is necessary for appropriate medical care. This study examines factors affecting testing and diagnosis of smell/taste disorders .

METHODS

The online USA Smell and Taste Patient Survey was made available to US patients with smell/taste disorders between April 6-20, 2022. 4,728 respondents were included.

RESULTS

1,791 (38%) patients reported a documented diagnosis. Patients most often saw family practitioners (34%), otolaryngologists (20%), and Taste/Smell clinics (6%) for smell/taste dysfunction. 64% of patients who went to Taste/Smell clinics received smell testing, followed by 39% of patients who saw otolaryngologists, and 31% of patients who saw family practitioners. Factors associated with increased odds of diagnosis included age (25-39 years (OR 2.97, 95% CI [2.25, 3.95]), 40-60 (OR 3.3, 95% CI [2.56, 4.52]), and >60 (OR 4.25, 95% CI [3.21, 5.67]) vs. 18-24 years), male gender (OR 1.26, 95% CI [1.07, 1.48]), insurance status (private (OR 1.61, 95% CI [1.15, 2.30]) or public (OR 2.03, 95% CI [1.42, 2.95]) vs. uninsured), perception of their family practitioner to be knowledgeable (OR 2.12, 95% CI [1.16, 3.90]), otolaryngologic evaluation (OR 6.17, 95% CI [5.16, 7.38]), and psychophysical smell testing (OR 1.77, 95% CI [1.42, 2.22]).

CONCLUSION

Psychophysical testing, otolaryngologic evaluation, patient assessment of family practitioner knowledge level, insurance, age, and gender are significant factors in obtaining smell/taste dysfunction diagnosis. This study identifies barriers to diagnosis including lack of insurance or access to specialist evaluation and highlights the importance of educating family practitioners in diagnosis and management of patients with smell/taste disorders.

Digital Health Data Quality Issues: Systematic Review

BACKGROUND

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

OBJECTIVE

The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.

RESULTS

The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.

CONCLUSIONS

The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Factors affecting the quality of diagnosis coding data with a triangulation view: A qualitative study

OBJECTIVE

The most important challenge in utilizing medical record codes is the quality of coding data. The present study aims to investigate factors affecting the quality of diagnosis coding from different aspects covering different stakeholders in a multi-dimensional approach.

METHODS

First, we used Conventional Content Analysis to maximally gather all effective factors. As such, semi-structured interviews were conducted with medical record coders (N = 32) at the referral hospitals in Mashhad, Iran. Second, 86 hospital staff members from 25 provinces were surveyed using a web-based questionnaire. Finally, a focus group discussion was conducted among coders (N = 18) in different hospitals across the country.

RESULTS

In general, the barriers to quality of inpatient record coding can be classified into three categories: (I) physician-related, (II) coder-related, and (III) managerial, financial and administrative factors.

CONCLUSION

A triangulation view (related to coders, physicians as well as managerial, financial and administrative dimensions) could be used to identify the barriers affecting the quality of diagnosis coding data. The results of this study may help policymakers in development and implementation of appropriate strategies and effective interventions to improve the quality of clinical coding.