ABSTRACT
BACKGROUND: Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. OBJECTIVE: This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS: A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. RESULTS: The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. CONCLUSIONS: The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42971.
ABSTRACT
BACKGROUND: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. OBJECTIVE: The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. METHODS: Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. RESULTS: Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. CONCLUSIONS: Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence.
ABSTRACT
RATIONALE & OBJECTIVE: Persons with end-stage kidney disease receiving in-center maintenance hemodialysis may be at high risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure and severe outcomes with coronavirus disease 2019 (COVID-19). The objective of this study was to examine the correlation of SARS-CoV-2 positivity rate per capita and COVID-19-associated deaths with number of dialysis stations and demographics of residents within zip codes in Cook County, IL. STUDY DESIGN: Ecological analysis. SETTING & PARTICIPANTS: Data for SARS-CoV-2 test results and COVID-19-associated deaths during January 21 to June 15, 2020, among the 5,232,412 residents living within the 163 zip codes in Cook County, IL, were merged with demographic and income data from the US Census Bureau. The total number of positive test results in this population was 84,353 and total number of deaths was 4,007. ASSESSMENTS: Number of dialysis stations and stations per capita within a zip code were calculated. SARS-CoV-2-positive test results per capita were calculated as number of positive test results divided by the zip code population. COVID-19-associated deaths per capita were calculated as COVID-19 deaths among residents for a given zip code divided by the zip code population. ANALYTIC APPROACH: Spearman rank correlation coefficients were calculated to examine the correlation of SARS-CoV-2-positive tests per capita and COVID-19-associated deaths per capita with dialysis stations, demographics, and household poverty. To account for multiple testing, statistical significance was considered as P < 0.005. RESULTS: Among the 163 Cook County zip codes, there were 2,501 dialysis stations. Positive test results per capita were significantly associated with number of dialysis stations (r = 0.25; 95% CI, 0.19 to 0.29; P < 0.005) but not with dialysis stations per capita (r = 0.02; 95% CI, -0.03 to 0.08; P = 0.7). Positive test results per capita also correlated significantly with number of households living in poverty (r = 0.57; 95% CI, 0.53-0.6; P < 0.005) and percentage of residents reporting Black race (r = 0.28; 95% CI, 0.23-0.33; P < 0.005) and Hispanic ethnicity (r = 0.68; 95% CI, 0.65-0.7; P < 0.001;). COVID-19-associated deaths per capita correlated significantly with the percentage of residents reporting Black race (r = 0.24; 95% CI, 0.19-0.29; P < 0.005) and with percentage of households living in poverty (r = 0.34; 95% CI, 0.29-0.38; P < 0.005). The association between the number of COVID-19-associated deaths per capita and total number of dialysis stations (r = 0.20; 95% CI, 0.14-0.25; P = 0.01) did not achieve a priori significance, whereas the association with dialysis stations per capita (r = 0.12; 95% CI, 0.07-0.17; P = 0.01) was not significant. LIMITATIONS: Analysis is at the zip code level and not at the person level. CONCLUSIONS: The number of dialysis stations within a zip code correlates with the SARS-CoV-2 positivity rate per capita in Cook County, IL, and this correlation may be driven by population density and the demographics of the residents. These findings highlight the high risk of SARS-CoV-2 exposure for patients with end-stage kidney disease living in poor urban areas.