Assessing Equitable Recruitment in a Digital Health Trial for Asthma

Factors Affecting Adherence in Allergic Disorders and Strategies for Improvement

Addressing patient adherence is a key element in ensuring positive health outcomes and improving health-related quality of life for patients with atopic and immunologic disorders. Understanding the complex etiologies of patient nonadherence and identifying real-world solutions is important for clinicians, patients, and systems to design and effect change. This review serves as an important resource for defining key issues related to patient nonadherence and outlines solutions, resources, knowledge gaps, and advocacy areas across five domains: health care access, financial considerations, socioenvironmental factors, health literacy, and psychosocial factors. To allow for more easily digestible and usable content, we describe solutions based on three macrolevels of focus: patient, clinician, and system. This review and interactive tool kit serve as an educational resource and call to action to improve equitable distribution of resources, institutional policies, patient-centered care, and practice guidelines for improving health outcomes for all patients with atopic and immunologic disorders.

Evaluation of a Primary Care-Integrated Mobile Health Intervention to Monitor between-Visit Asthma Symptoms

OBJECTIVES

 This study aimed to evaluate implementation of a digital remote symptom monitoring intervention that delivered weekly symptom questionnaires and included the option to receive nurse callbacks via a mobile app for asthma patients in primary care.

METHODS

 Research questions were structured by the NASSS (Nonadoption, Abandonment, Scale-up Spread, and Sustainability) framework. Quantitative and qualitative methods assessed scalability of the electronic health record (EHR)-integrated app intervention implemented in a 12-month randomized controlled trial. Data sources included patient asthma control questionnaires; app usage logs; EHRs; and interviews and discussions with patients, primary care providers (PCPs), and nurses.

RESULTS

 We included app usage data from 190 patients and interview data from 21 patients and several clinician participants. Among 190 patients, average questionnaire completion rate was 72.3% and retention was 78.9% (i.e., 150 patients continued to use the app at the end of the trial period). App use was lower among Hispanic and younger patients and those with fewer years of education. Of 1,185 nurse callback requests offered to patients. Thirty-three (2.8%) were requested. Of 84 PCP participants, 14 (16.7%) accessed the patient-reported data in the EHR. Analyses showed that the intervention was appropriate for all levels of asthma control; had no major technical barriers; was desirable and useful for patient treatment; involved achievable tasks for patients; required modest role changes for clinicians; and was a minimal burden on the organization.

CONCLUSION

 A clinically integrated symptom monitoring intervention has strong potential for sustained adoption. Inequitable adoption remains a concern. PCP use of patient-reported data during visits could improve intervention adoption but may not be required for patient benefits.

Patient-Clinician Diagnostic Concordance upon Hospital Admission

OBJECTIVES

 This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician.

METHODS

 Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance.

RESULTS

 A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 "R-code") for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p < 0.01), respectively.

CONCLUSION

 About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 "R-code" entered as the principal problem and patient-reported lack of confidence may predict patient-clinician nonconcordance early during hospitalization via this approach.

Health disparities in allergic diseases

PURPOSE OF REVIEW

Healthcare disparities impact prevalence, diagnosis, and management of allergic disease. The purpose of this review is to highlight the most recent evidence of healthcare disparities in allergic conditions to provide healthcare providers with better understanding of the factors contributing to disparities and to provide potential management approaches to address them. This review comes at a time in medicine where it is well documented that disparities exist, but we seek to answer the Why , How and What to do next?

RECENT FINDINGS

The literature highlights the socioeconomic factors at play including race/ ethnicity, neighborhood, insurance status and income. Management strategies have been implemented with the hopes of mitigating the disparate health outcomes including utilization of school-based health, distribution of educational tools and more inclusive research recruitment.

SUMMARY

The studies included describe the associations between upstream structural and social factors with downstream outcomes and provide ideas that can be recreated at other institutions of how to address them. Focus on research and strategies to mitigate healthcare disparities and improve diverse research participant pools are necessary to improve patient outcomes in the future.

[Developments in the digitalization of public health since 2020 : Examples from the Leibniz ScienceCampus Digital Public Health Bremen]

Digital public health has received a significant boost in recent years, especially due to the demands associated with the COVID-19 pandemic. In this report, we provide an overview of the developments in digitalization in the field of public health in Germany since 2020 and illustrate these with examples from the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH).The following topics are central: How do digital survey methods as well as digital biomarkers and artificial intelligence methods shape modern epidemiology and prevention research? What is the status of digitalization in public health offices? Which approaches to health economics evaluation of digital public health interventions have been utilized so far? What is the status of training and further education in digital public health?The first years of the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH) were also strongly influenced by the COVID-19 pandemic. Repeated population-based digital surveys of the LSC indicated an increase in use of health apps in the population, for example, in applications to support physical activity. The COVID-19-pandemic has also shown that the digitalization of public health enhances the risk of misinformation and disinformation.

Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis

BACKGROUND

Asthma is a common cause of morbidity and mortality in children. Predictive models may help providers tailor asthma therapies to an individual's exacerbation risk. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown.

OBJECTIVE

Determine the impact of an electronic health record (EHR) vendor-released model on outcomes for children with asthma.

METHODS

The Epic Systems Risk of Pediatric Asthma Exacerbation model was implemented on February 24, 2021, for volunteer pediatric allergy and pulmonology providers as a noninterruptive risk score visible in the patient schedule view. Asthma hospitalizations, emergency department (ED) visits, or oral steroid courses within 90 days of the index visit were compared from February 24, 2019, to February 23, 2022, using a difference-in-differences design with a control group of visits to providers in the same departments. Volunteer providers were interviewed to identify barriers and facilitators to model use.

RESULTS

In the intervention group, asthma hospitalizations within 90 days decreased from 1.4% (54/3,842) to 0.7% (14/2,165) after implementation with no significant change in the control group (0.9% [171/19,865] preimplementation to 1.0% [105/10,743] post). ED visits in the intervention group decreased from 5.8% (222/3,842) to 5.5% (118/2,164) but increased from 5.5% (1,099/19,865) to 6.8% (727/10,743) in the control group. The adjusted difference-in-differences estimators for hospitalization, ED visit, and oral steroid outcomes were -0.9% (95% confidence interval [CI]: -1.6 to -0.3), -2.4% (-3.9 to -0.8), and -1.9% (-4.3 to 0.5). In qualitative analysis, providers understood the purpose of the model and felt it was useful to flag high exacerbation risk. Trust in the model was calibrated against providers' own clinical judgement.

CONCLUSION

This EHR vendor model implementation was associated with a significant decrease in asthma hospitalization and ED visits within 90 days of pediatric allergy and pulmonology clinic visits, but not oral steroid courses.