Track topics on Twitter Track topics that are important to you
The purpose of the current study is to test different interventions to determine the most effective way to promote flu vaccine uptake in a high-risk population identified by an "artificial intelligence" (AI) or machine learning (ML) algorithm. The specific aims are:
1. Evaluate the effect on flu vaccination rates of informing health-system patients who are identified by an ML analysis of EHR data to be at high risk for flu complications that they are at high risk with either (a) no additional explanation, (b) an explanation that this determination comes from an analysis of their medical records, and (c) the additional explanation that an AI or ML algorithm made this determination.
2. Evaluate the effects of the same three interventions on diagnoses of flu in the same patients.
On average, 8% of the US population gets sick from flu each flu season (Tokars et al. 2018). Since 2010, the annual disease burden of influenza has included 9-45 million illnesses, 140,000-810,000 hospitalizations, and 12,000-61,000 deaths (CDC 2020). The CDC recommends the flu vaccination to everyone aged 6+ months, with rare exception; almost anyone can benefit from the vaccine, which can reduce illnesses, missed work, hospitalizations, and death (CDC 2019a). Flu vaccination will be especially important for high-risk patients during the COVID-19 pandemic so that flu cases are reduced and resources conserved.
While most recover from influenza without treatment, the elderly, those with comorbidities, and other high-risk individuals can experience complications such as pneumonia, other respiratory illness, and death. Geisinger, a large health system in Pennsylvania and New Jersey, has partnered with Medial EarlySign (Medial; www.earlysign.com) to develop a machine learning (ML) algorithm to identify patients at risk for serious (moderate to severe) flu-associated complications on the basis of their existing electronic health record (EHR) data. Geisinger will deploy this system during the 2020-21 flu season and contact the identified patients with special messages (in addition to standard efforts made by the health system every flu season) to encourage vaccination. Flu vaccination will be especially important for high-risk patients during the COVID-19 pandemic so that flu cases are reduced and resources conserved.
Published results suggest Medial's ML systems identify high-risk patients in other contexts (Goshen et al., 2018; Zack et al., 2019). However, there is little evidence about (a) whether informing patients they are at high risk makes them more likely to receive vaccination; (b) how patients react to being told their risk status is the result of an analysis of their health records; and (c) whether informing patients their risk status has been determined by an "algorithm," by "machine learning," and/or by "artificial intelligence" will increase or decrease their likelihood of getting vaccinated. This study will address these gaps in the literature, which are especially important in light of the anticipated future growth of AI/ML system use throughout healthcare.
Medial's algorithm is an example of how interoperable health information exchange (HIE)—the ability for health information technology to share patient data—can improve the efficiency and effectiveness of healthcare. However, patients may not appreciate these benefits or the fact that healthcare has become substantially more integrated and collaborative. A systematic review of patient privacy concerns about HIE found that 15-74% of patients expressed privacy concerns, depending on the study, and concluded that patient perspectives remain poorly understood. A flu outreach message that explicitly references a review of patient medical records might backfire as patients react badly to a sense they have lost control of their health records.
There is conflicting evidence on how people respond to advice or information that comes from an algorithm or machine. Dietvorst et al. (2015) documented a pattern of "algorithm aversion," in which people choose inferior human over superior algorithmic forecasts, especially after they observed the algorithm make an error. In contrast, Logg et al. (2018) described "algorithm appreciation," in which people followed advice more when they thought it came from algorithms than when they thought it came from human beings. Finally, Bigman and Gray (2019) found aversion to algorithms that make "moral decisions," including a (fictitious) medical decision of choosing whether or not to operate on a high-risk patient. In the current setting, the algorithm is merely advising patients on taking an action (an annual flu shot) that is already the standard of care, and there is no opportunity to observe an erroneous recommendation, so the hypothesis is that "algorithm appreciation" will cause people to react positively to being informed of the algorithm's role. Thus, this study will address two important research questions:
1. Does informing patients that they are at high risk for flu complications (a) increase the likelihood that they will receive flu vaccine; and (b) decrease the likelihood that they receive diagnoses of flu and/or flu-like symptoms in the ensuing flu season?
2. Does informing patients that their high-risk status was determined (a) by analyzing their medical records (vs. by no specified method); and (b) by an AI/ML algorithm* analyzing their medical records (as opposed to via unspecified methods or human medical records analysis) affect the likelihood that they receive the flu vaccine and/or diagnoses of flu and/or flu-like symptoms in the ensuing flu season?
Our specific aims are:
1. Evaluate the effect on flu vaccination rates of informing health-system patients who are identified by an ML analysis of EHR data to be at high-risk for flu complications that they are at high risk with either (a) no additional explanation, (b) an explanation that this determination comes from an analysis of their medical records, and (c) the additional explanation that an AI or ML algorithm made this determination.
2. Evaluate the effects of the same three interventions on diagnoses of flu in the same patients.
Included in the study will be current Geisinger patients 18+ years of age with one or more visits to a Geisinger primary care physician (PCP) between January 1, 2008 and January 30, 2020 and no contraindications for flu vaccine. Medial will provide flu-complication risk scores from their ML algorithm (based on coded EHR data), on the basis of which the top 3% of patients at highest risk will be included. Based on prior behavior and other predictors in a second ML model, Medial will also provide the likelihood each patient will get vaccinated during the study flu season; these values and the primary risk scores will be used as covariates in exploratory data analyses. The anticipated number of patients in the top 3% of risk is 16,500.**
According to the CDC (https://www.cdc.gov/flu/fluvaxview/coverage-1819estimates.htm), approximately 68% of people age 65+ are vaccinated each year, so this will be used in a power analysis as a proxy base rate for a control condition. The study will have 84% power to detect a 3% absolute difference or greater in the vaccination outcome between conditions (68% vs 71%, two-tailed alpha of .05), on the assumption that each condition will have 16,500/4=4125 patients. For the rarer outcome of flu diagnosis, the study will have 86% power to detect a 1.2% difference or greater—from an estimated 3.9% rate in this high-risk population (based on the CDC estimate for people age 65+ [Tokars et al., 2018]) to a 2.7% rate.
The primary study outcomes will be the rates of flu vaccination and flu diagnoses during the 2020-21 season (September-March) by targeted patients. Secondary, exploratory outcomes will also be measured: The rates of flu vaccination and diagnoses by non-targeted first-degree relatives (parents, spouses/partners, children) of targeted patients; the rates of flu complications and flu-like symptoms among targeted patients and relatives; and rates of other relevant healthcare utilization outcomes such as ER visits and hospitalizations.
Generalized linear mixed models (GLMMs) will examine the primary study outcomes as a function of the study arms (between-subjects), with patient-visited PCPs and/or clinics included as random effects variables, assuming high intraclass correlation coefficients. GLMMs will specify a binary distribution and log-link function in the case of dichotomous outcome variables (e.g., flu vaccination, flu diagnosis), and a negative binomial distribution and log-link function in the case of any highly positively skewed count variables such as ER visits and hospitalizations (where over-dispersion typically remains in the case of a Poisson distribution model). For these exploratory analyses, within-patient change (from the same period one year earlier) will also be analyzed. Also, each patient will receive the same type of communication (a/b/c/d) via up to three modalities—printed letter to their mailing address, SMS to their mobile phone, and/or secure message via Geisinger's patient portal—depending on what information is on file for each patient. The communication channels used for each patient will be covariates in later analyses.
*Note: The study will not necessarily use the terms "AI," "ML," or "algorithm" in the messages to groups b, c, and d; instead, these messages will be designed to be readable and comprehensible by the patient audience while still including the key concepts that differentiate the interventions from one another.
**Note: Sample size (n) will be defined by the top x% of patients at risk for flu complications. It is anticipated that x=3 and n=16,500, and these conservative assumptions are used throughout this entry. It is conceivable that x>3 and 16,500>n≤45,000; in that case, the investigators will update the record accordingly.
Risk reduction, Medical records-based recommendation, Algorithm-based recommendation
Not yet recruiting
Published on BioPortfolio: 2020-04-01T04:26:54-0400
Computer-aided diagnostic software has been used to assist physicians in various ways. Text-based prediction algorithms have been trained on past medical records through data mining and fe...
This is a clinical trial. The purpose of this clinical trial is to see if study participants have better health outcomes if their pharmacist has access to their medical records. The study ...
This study implements an extended (post marked) examination of the efficacy of a proactive auto-CPAP algorithm, which is based on the forced oscillation technique (FOT) in a sample of pati...
The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Ce...
The study ia aiming to the assessment of Mid-Regional proadrenomedullin (MR-proADM) as a novel biomarker that can provide accurate short-, mid- and long term prognostic information in the ...
Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the priva...
Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is...
A standardized definition for serious opioid overdose has not been clearly established for disease surveillance or assessing the impact of risk mitigation strategies. The purpose of this study was to ...
Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is en...
Graph-based dimensionality reduction methods have attracted substantial attention due to their successful applications in many tasks, including classification and clustering. However, most classical g...
Computer-based systems for input, storage, display, retrieval, and printing of information contained in a patient's medical record.
Removal of a MEDICAL DEVICE from the market due to the identification of an intrinsic property of the device that results in a serious risk to public health.
Reduction of high-risk choices and adoption of low-risk quantity and frequency alternatives.
The creation and maintenance of medical and vital records in multiple institutions in a manner that will facilitate the combined use of the records of identified individuals.
Hospital department responsible for the creating, care, storage and retrieval of medical records. It also provides statistical information for the medical and administrative staff.
A vaccine is a biological preparation that improves immunity to a particular disease. A vaccine typically contains an agent that resembles a disease-causing microorganism, and is often made from weakened or killed forms of the microbe, its toxins or one ...
Alternative Medicine Cleft Palate Complementary & Alternative Medicine Congenital Diseases Dentistry Ear Nose & Throat Food Safety Geriatrics Healthcare Hearing Medical Devices MRSA Muscular Dyst...