End Diagnostic Overshadowing (EDO)

End Diagnostic Overshadowing: Understanding and Reducing Diagnostic Error in Patients With Disabilities

The goal of this study is to address the critical issue of diagnostic overshadowing by applying the Collective Impact Model40 to co-produce our End Diagnostic Overshadowing program with academic, health systems, health professional, PWDs, family members, and community stakeholders. Through this work, we will identify and address mechanisms that contribute to diagnostic overshadowing and diagnostic errors among people with disabilities. The main questions to answer are whether knowledge about diagnostic errors and confidence will improve with health care providers and professionals involved in diagnostic provesses, whether developed algorithms to identify patients at risk of diagnpstic error will be used, whether there will be change in time to diagnostic evaluation for PWD from the specified 5 population groups and with the specified diagnoses prone to error, and whether changes in usage of CPT Evaluation and Management codes will occur.

Study Overview

• Status: Recruiting
• Conditions: Disabilities Multiple
• Intervention / Treatment: Other: Standard of care; Behavioral: Electonic health record prompts with education

Detailed Description

People with disabilities (PWD) experience increased risk of diagnostic error-sometimes due to attributing symptoms to disability rather than a potentially new or co-morbid conditions. As well, some diagnoses are prone to error. Based on literature we identified the following twenty-six diagnoses prone to error with ICD-10 codes: Aortic aneurysm and dissection I71.0 - I71.9; Arterial thromboembolism I74.0 - I74.9; Venous thromboembolism I82.0-I82.99 and I82.A-I82.C; Congestive heart failure I50.1-150.9; Stroke All I60, I61, I62, I63, I64; Myocardial infarction I21.0-I21.9 and I21.A-I21.B; Spinal abscess G06.0, G06.1 and G06.2; Meningitis and encephalitis G04 -G04.91; Endocarditis I33.0-I33.9 and I38; Sepsis A41.0-A41.9; Pneumonia J12.0-J95.851; Lung cancer C34.0-C34.92; Melanoma C43.0- C43.9; Colorectal cancer C18.0-C18.9; Breast cancer C50 to C50.929, and C79.81; Prostate cancer C61; Pediatric Arterial ischemic stroke I63.0-163.9xx; Appendicitis K35-K35.8xx; Asthma J45.2-J45.998; Retinal blastoma C69.20, C69.21, C60.22; Brain tumor C71.0-C71.9; Polyateritis M30.0-M30.8; Congenital heart disease Q20 - Q28 (Q24.9 particularly important); Duchense muscular dystrophy G71.0-G71.9; Inflammatory bowel disease K51.0-K51.9; Scleroderma M34.0-M34.9.The goal of this research is to identify and create understanding of what underlies and contributes to increased risk of diagnostic error with these diagnoses. The investigators plan to develop ways to reduce diagnostic error, specifically ways to identify people with disabilities at risk of diagnostic error (DE). The investigators will also develop education programs and decision supports targeted to healthcare professionals. If it is effective, ways to reduce diagnostic error will have been developed among people with disabilities.

Aim 1: Identify and create understanding of mechanisms underlying diagnostic overshadowing. We will conduct baseline and post-analysis of CPT codes related to diagnostic processes to examine differences between patients aged ≥3 years with and without the specific disabilities listed above, along with demographic and clinical characteristics associated with health outcomes (e.g., race, ethnicity, gender, insurance type, specific diagnoses). Based on differences identified in baseline CPT analyses, we will conduct follow-up chart reviews and targeted interviews and develop Joint Commission-style individual mock tracers, following the care of the listed specific populations of PWD, and systems-of-care tracers focused on evaluating the extent to which care systems incorporate accessibility, effective communication, reasonable accommodations, trauma-informed care, and other processes that support timely and accurate diagnosis of conditions prone to diagnostic error. Mock tracer teams will provide formative evaluation of care to involved staff. Using inductive thematic analysis45 of notes from chart reviews, interviews, and mock tracers, we will identify mechanisms underlying diagnostic overshadowing. We will evaluate CPT codes (quantitative), chart reviews (mixed methods), and interview and tracer results (qualitative) at Year 5 compared with Year 1 to determine changes.

Our hypothesis is that there will be statistical difference in diagnostic processes between people with the specified disabilities and people without the specified disabilities.

Aim 2: Co-produce a framework of mechanisms underlying diagnostic overshadowing to develop educational programs and EHR decision supports. We will collaborate with stakeholders to refine, confirm, and prioritize mechanisms underlying diagnostic overshadowing identified in Aim 1 and use these findings for the co-production of educational programs and EHR decision supports. We will evaluate these mitigation efforts through: (1) pre- and post-knowledge assessments related to use of the educational programs; and (2) descriptive pre- and post-data on the use of specific EHR decision supports.47 Our hypothesis is that we will have information that can be used to develop algorithms for identifying PWD from the specific populations at risk of DO/DE as evidenced by diagnostic process data from the Safer DX Checklist and usage of CPT E/M code.

Our Hypotheses are that there will be statistical change in time to diagnostic evaluation for PWD from the specified 5 population groups and with the specified diagnoses prone to error. We will Evaluate for change after implementation of algorithms to identify patients with the specified disabilities at risk for DO/DE.

Our research is innovative and fills a critical need for improving health outcomes among PWD. We will integrate CPT code analysis with Joint Commission recommendations in a novel way to establish a system for identifying mechanisms underlying diagnostic overshadowing and developing and evaluating targeted educational programs and EHR decision supports to reduce diagnostic errors.

Evaluation of diagnostic processes

Safer DX Checklist

The Safer DX checklist was developed to guide chart reviews including patient history, examination, diagnostic test interpretation and follow-up, ordering of tests, referrals, and diagnostic assessment. The checklist is used to assess five main aspects of the diagnostic process (1) the patient-provider encounter; (2) use and interpretation of diagnostic tests; (3) follow-up and tracking of diagnostic information; (4) referrals and follow-up; and (5) patient-related factors. It is used in multiple studies addressing diagnostic error.We modified the Safer DX Checklist for use by nurses.

CPT Evaluation and Management codes

CPT codes were developed by the American Medical Association and undergo periodic revisions and ongoing maintenance. CPT codes are the universal way that providers document their services, providing standardized reporting needed for billing and reimbursement of healthcare providers, including physicians, nurse practitioners, physician assistants, other professionals.66 The system provides numeric codes for issues such as: 1. The site of service (e.g., Emergency Department, inpatient, outpatient, preventive services); 2. The service provided; 3. The complexity of clinical information-gathering and decision-making, and 4) Time spent. The accuracy of CPT codes can vary, as indicated in a study of CPT codes related to hip fractures in the National Surgical Quality Improvement Program.68 However, CPT codes provide a standardized database used to report aggregated outcomes and to highlight potential problem areas/issues needing further investigation. CPT E/M codes usage is a stage in the diagnostic process where errors can occur.

CPT E/M codes are used to bill for services by providers related to the diagnostic process in evaluating and managing the health of a patient. Each setting has a specific group of CPT E/M codes ranges from lowest time and complexity of decision making to highest time and complexity of decision making. A recent study used CPT E/M codes for video telehealth visits compared to in-person visits with established patients of a large urban public healthcare system above the 50th percentile in video telehealth utilization. Evaluation indicated lower complexity of E/M with telehealth visits.

Furthermore, use of CPT E/M codes at telehealth visits varied by specialty, but the authors noted that it was not known if the differences were due to the two types of visits or to the comfort level of providers and patients. In a small study using emergency department (ED) data at Rush University Medical Center (July 1, 2019 to December 1, 2020), differences in CPT E/M codes were found between patients with and without IDD visiting the ED for the same reasons and same level of severity. Moderate (99284) and high complexity (99285) evaluation and management codes, with no differences in time-intensive cases, were used with 25.7% of patients with IDD compared to 39.6% of patients without IDD, with statistical significance. Additionally, median professional charges for patients with IDD were lower. An analysis of differences in use of CPT E/M codes shows promise in using them to identify and understand mechanisms underlying diagnostic overshadowing and diagnostic error. In our chart reviews we will collect data on any use of CPT E/M codes including dates and specific codes used.

Mock tracers Mock tracers were developed by the Joint Commission for use in preparing for accreditation visits and are often used in healthcare systems as part of ongoing quality assurance and professional development efforts. Mock tracers provide information on patient experiences, quality of care, healthcare processes and products, and areas needing improvement. Tracers involve one-on-one and small group interviews with prompts for the questions that will be asked in addition to a review of patient charts and forms. For this project, questions will center around diagnostic processes (as evaluated using the Safer DX Checklist and evaluation of the use of CPT codes. Deeper inquiry is expected based on answers.

Algorithms

We recognize that lack of data on people with disabilities can lead to inadequate algorithms . People with disabilities expressed concerns of being denied life-saving health services during COVID related to crisis triage algorithms that didn't reflect their needs. Algorithms are already in use to address diagnostic error such as identifying patients at risk of delayed test results; delays in follow up of chest imaging results tests for hypothyroidism, and delayed/missed diagnoses related to abdominal pain. The study that addressed missed/delayed diagnoses related to abdominal pain was conducted in an ED. An algorithm was developed to identify patients at high risk of diagnostic error related to abdominal pain and then used for chart reviews to identify patients at high-risk for diagnostic error related to abdominal pain. In a randomized clinical trial, algorithms were used to prospectively identify patients at high risk of delayed/missed diagnoses of lung, colorectal or prostate cancer. Time to diagnostic evaluation was significantly reduced in the intervention group vs. control group for colorectal and prostate cancers, but not lung cancers. None of the research on algorithms specifically addressed diagnostic overshadowing as part of delayed/missed diagnoses and none addressed intersectionality. A 2009 study specifically on educing diagnostic overshadowing found case studies for educational purposes to be useful.

EHR prompts and alerts

The stage of the diagnostic process (e.g., obtaining clinical history, conducting exams, ordering specific tests, assessments, developing diagnoses, post-diagnostic referrals) requires different clinical decision supports; Furthermore, conditions that are not common require specific supports. Through standard order sets, alerts and reminders, and other means of diagnosticsupport (e.g., website), clinicians can access guidelines more easily. However, poorly designed EHR support can contribute to diagnostic error.82-84 Therefore, developing EHR decision supports requires attention to issues such as how the supports are accepted by clinicians, how they fit with workflow, time requirements, formatting, and how supports promote system-thinking. The EPIC EHR system, as an example, provides a means to improve decision support. Further, patient participation is important, and EHR systems can provide decision supports that can be used by both clinicians and patients.86

Co-production of healthcare programs

Involving impacted persons in co-production of services impacting them is considered an ethical issue in healthcare, transcending the traditional dichotomy between knowledge and program developers and users. Co-production involves building collaboration of people from impacted groups in the production and use of knowledge and programs from the start of the process. Participation of PWD impacted by the results of research and program planning is often limited to providing input after key decisions have already been made rather than throughout the process. However, beginning work to involve people with IDD in the co-production of programs for behavioral health indicated improvements in social networks and confidence for participation. In addition, co-production has been used in developing a framework in healthcare quality improvement. In co-production, the team will work with academic, health systems, health professional, PWDs, family members, and community stakeholders.

Participatory Planning and Decision-Making (PPDM) process versus structured focus groups

To guide development of educational materials addressing diagnostic error, the original plan for was to conduct a new Participatory Planning and Decision-Making (PPDM) process. As we have advanced in our work , two issues led us to revise this approach in favor of structured focus groups with individuals with disabilities and other stakeholders to bring real-world experiences and themes to inform our algorithms and clinical decision-support tools and related targeted education programs The two issues were; 1) An integrative review of diagnostic-error interventions (2017-2024), indicated that the core themes identified from the earlier Administration for Community Living -funded PPDM process continue to be valid and foundational. These themes include the need for dedicated education in the care of individuals with disabilities; the importance of interprofessional education; critique of the medical model of disability and attention to social, civil-rights, and other inclusive models; attention to intersectionality; meaningful involvement of individuals with disabilities in program design, implementation, and evaluation; and the use of experiential pedagogical approaches. Repeating a full PPDM process would be duplicative and would not yield substantially new themes. 2) End Diagnostic Overshadowing program educational materials will need to be targeted to specific audiences-patients, community members, health-care staff, and advanced practice providers. At this stage, the most useful input is not the generation of new themes, but the development and evaluation of educational materials using current themes. Structured focus groups using an organized set of questions are better suited to this type of targeted, content-specific feedback.

Study endpoints:

Primary

At Year 5 compared to Year 1, diagnostic process usage with the five identified groups of people with disabilities (quantitative) will be evaluated for changes following implementation of algorithms to identify people with disabilities at risk of DO/DE along with EHR decision supports and prompts/alerts on specific issues. We expect statistical changes.

We will evaluate our education programs through 1) pre- and post- knowledge checks of usage and 2) descriptive data on use of specific EHR decision supports.101 We expect statistical change in knowledge.

We plan time to conduct pre-post time to diagnostic for 2-3 issues still yet to be determined to address delayed/missed diagnoses. We expect time to diagnostic evaluation to decrease.

Secondary For quantitative measures (Safer DX Checklist data, CPT E/M code usage analysis, knowledge checks, descriptive data on use of EHR decision supports and prompts/alert), we will use ANCOVA to probe for interaction effects using pre-test measures and independent variables from Year one: age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, insurance type, severity of illness [relevant to site]), and the 10 issues suggested by stakeholders (singly or in composites) as covariates and evaluate for variation in post-test results. Interaction effects are expected.

Final mock tracers will be conducted at the end of Year 4 and in Year 5. Tracer notes will be compared to notes before the intervention using qualitative analysis. Changes will provide context for any changes in quantitative measures.

Framework The Collective Impact Model for social change is the organizing framework for this project. Multiple impacted groups are brought to bear on the problem of diagnostic overshadowing. Previous efforts to develop algorithms to identify patients at high risk of diagnostic error have not specifically addressed diagnostic overshadowing affecting patients with disabilities and have not previously addressed intersectionality. The Collective Impact Model has not previously been used to address diagnostic overshadowing or the overall problem of diagnostic errors. The following five tenets must be met to facilitate organization and planning with multiple impacted groups for a Collective Impact project: 1) achieving a common agenda; 2) ensuring continuous Communication; 3) identifying shared measurement strategies; 4) employing mutually reinforcing activities to deliver programs and services that will achieve the intended outcome of Collective Impact efforts; and 5) employing a dedicated staff as backbone support. Partnering requires attention to bringing in the experiences and voices of all impacted groups.

Building Organizational Structures using the Collective Impact Model In the first six months, members of research team will meet at least once a month to solidify the team, hire new staff, and create structures based on the Collective Impact Model. A Cross-Sector Partnership Steering Committee, the Cross-Disability Advocate Advisory Committee, and three Consortium Action Networks (Communication, Measurement, Education) will be organized. The Steering Committee and Cross-Disability Advocate Committee will take overall accountability for developing a shared agenda (Collective Impact Tenet 1). Practices that improve understanding of diagnostic overshadowing and the identification of underlying mechanisms will be developed through continuous communication. The Communication Action Network will take accountability (Collective Impact Tenet 2). The Measurement Action Network will take accountability for ongoing evaluation and final evaluation in Year 5(Collective Impact Tenet 3). The Education Action Network will take accountability for facilitating development of targeted education programs and EHR decision supports to mitigate diagnostic overshadowing (Collective Impact Tenet 4). The developed infrastructure will facilitate mutually reinforcing activities that encourage the sharing of perspectives and best practices of the project's interdisciplinary partners. Processes leading to the achievement of project goals and outcomes will be facilitated by dedicated backbone staff who will assist with the management, planning, and logistics required by the project. RUSH University is the lead institution, and each consortium partner has specific responsibilities. (Collective Impact Tenet 5).

Design Aim 1: Identify and create understanding of mechanisms underlying diagnostic overshadowing.

Introduction: Partnership will be built between three not for profit medical center systems that place prominence on improving the health of the populations they serve. RUSH University System for Health and affiliated RUSH University Medical Center, RUSH Oak Park Hospital and RUSH Copley Medical Center; Rochester Regional Health and affiliated Rochester General Hospital; and Erie County Medical Center (ECMC). These will be sites for pre-post analysis of diagnostic error among PWD via use of the Safer DX Checklist and CPT E/M code data, implementation of mock tracers, and then implementation of targeted education programs and EHR decision supports. Data use agreements between the three institutions are being obtained.

Data Collection will be in three steps. For the first, data were retrieved for period January 1, 2023 - June 30, 2024 for patients aged 3-89 who at any time had one or more of the 26 diagnoses prone to error from RUSH University Medical Center, RUSH Oak Park Hospital, RUSH Copley Hospital, and associated outpatient practices. These dates were chosen as changes were made to CPT E/M codes in 2023 in a way expected to reduce burden. Data are from cases of patients from and not from the specified disability groups. Data are being used to compare diagnostic processes for patients with and without the specified disabilities. To compare usage of CPT E/M codes data were retrieved on patients who received a billed CPT E/M code from the Emergency Departments (codes 99281-99285), from inpatient services (99221-99223, 99231-99233, 99238-99239), from outpatient visits with new patients (99202-99205), with established patients (99211-99215), and for preventive care (99384-99387). E/M is a stage in the diagnostic process where errors can occur. Quantitative methods for evaluation of CPT E/M codes data will be used. Data will be programmed with variable range checks and skip rules and will be exported in an automated manner into SPSS. Based on experience, patients with the specific disabilities will be identified through a comprehensive list of secondary diagnosis codes for the specific disabilities for patients aged 3-89 years old. Data will be age-disaggregated in groupings of five years, except the group aged 3-5 years old. All variables will be checked for errant values. Descriptive statistics will be computed for all items (CPT E/M codes), and distributions examined for non-normality and outliers. Descriptive statistics for all measures will be reported. For each type of visit, the investigators will first compare the overall proportion of each CPT E/M codes by disability status using pairwise Fisher's exact tests with a descriptive analysis of case frequency, age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, insurance type, and severity of illness [relevant to site]). Evaluation will be conducted on whether data can be collapsed into values of higher level and lower level complexity of evaluation and management codes. If so, binary logistic regression analysis will be conducted using the outcome of higher level and lower level of complexity of CPT evaluation and management codes and addressing the influence of race, ethnicity, gender, age ranges, disability type, insurance status, severity of illness and 10 chart review questions (previously described). Otherwise, the investigators will use ordinal regression analysis on the outcomes of the CPT E/M codes (using all levels of complexity). Site-specific analyses will be conducted (ie. ED, inpatient, outpatient, preventive care) and a combined model that accounts for site using cluster-robust standard errors.

Binary (or ordinal) regression analysis will be conducted for each setting (Emergency Department, inpatient, outpatient, preventive care) with the dependent variable being E/M codes (separately or split into lower and higher complexity codes). Independent variables will be demographics of age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, insurance type, and severity of illness [relevant to site]), and the 10 chart review issues listed above. Variable loadings will be assessed for use in developing algorithms for PWD from the specified groups at risk of diagnostic overshadowing.

We also plan to use Coincidence Analysis, to evaluate necessary and sufficient factors predicting diagnostic error, noting that two members of the team have taken training in usage of this type of analysis. Coincidence analysis is a configurational comparative method that historically was used primarily in the social sciences and is increasingly used in implementation science. Unlike traditional statistical methods that focus on the net effects of individual variables, coincidence analysis emphasizes the interplay of multiple factors and seeks to identify factors that might affect an outcome through patterns of co-occurrence. Coincidence analysis uses Boolean algebra in the identification of one or more combinations of minimally sufficient and necessary factors contributing to an outcome that may lack pairwise correlation used in regression analysis.

For the second step we will identify charts of patients in the specific disability groups and with specific diagnoses prone to error for retrospective manual chart reviews to improve identification of underlying mechanisms of diagnostic overshadowing. There is existing literature on diagnoses prone to error. At each partnering hospital (5) associated with the three medical centers, we will review at least five charts of patients from each of the five specific We will also develop a profile of patients at populations of people with disabilities from which we are collecting data (25 at each of the five institutions and associated outpatient practices). Considering additional targeted chart reviews we expect another 100. We will evaluate for issues which may provide insight into diagnostic overshadowing. We will use the Safer DX Checklist in the chart reviews. In discussions with staff, suggestions to explore include the same issues listed above in previous work. We will take notes on the ten issues. Notes will be evaluated for themes using inductive thematic analysis. The team will meet weekly to discuss themes. If, during the chart reviews, we determine other issues, we will submit an amendment to evaluate additional issues. We are developing protocols and training for the chart reviews with drafts that consider experiences of two previous groups in place. Protocols will be shared with the IRB

Third, with these baseline data that indicated higher rates of sepsis among patients with disabilities, we consulted with our Measurement Action Network and with our Advocate Advisory Committee and developed a system of care mock tracer focused on sepsis care with attention to people with disabilities that were implemented in the Emergency Departments at RUMC, Rush Oak Park, and Rush Copley. We also modified the Safer DX for use by nurses and were able to use the modified version in chart reviews. We will also develop separate mock tracers following the care of each specific population of PWD prone to diagnostic overshadowing. We will conduct 25 retrospective tracers of patients (5 each from the specific populations listed above with 10 tracers to be among children and at least 10 to be among patients from marginalized racial/ethnic groups) at each of the five partnering institutions with associated outpatient practices (at RUSH University Medical Center, RUSH Oak Park Hospital, and RUSH Copley Hospital) In our meetings with Rochester Regional Health and Erie County Medical Center we decided to determine numbers once we have experience in the Rush System.

We will inform the IRB once decisions are made. For tracers, we will also choose patients with issues such as high acuity, complexity of care (e.g., multiple tests, surgeries), transfers between units, and history of trauma. We recognize that issues affecting care differ by population. Therefore, we will develop a system of care tracer focused on understanding system facilitators and barriers to reducing diagnostic overshadowing including trauma-informed care as part of the system of care. As we will develop the mock tracers at Rush, we will revise/develop and conduct the first baseline tracers at Rochester Regional in Year two. We expect 75 tracers at baseline in the Rush system with numbers at Rochester Regional Health to be determined based on experience. We will inform the IRB once determined. We will use developmental formative evaluation methods for the mock tracers using guides, with the formative evaluation communicated to the respective units and practices.11 During years 3 and 4, we will conduct mock tracers in EDs (nationally 70% of inpatients at hospitals are processed through EDs), selective inpatient units (including pediatrics), and selective outpatient practices (including pediatrics). We will conduct final mock tracers and analysis at the end of Year 4 and in Year 5. We will conduct qualitative analysis of review notes compared to notes before intervention. We expect changes that will provide qualitative data on context of changes in quantitative measures.

The development and first baseline implementation of mock tracers and any edits will be completed at Rush by the beginning of Year three. At other systems, mock tracers will be conducted in year three. This will be a total of 125 tracers at baseline. With guides, developmental formative evaluation methods will be used for the mock tracers, with the formative evaluation communicated to the respective units and practices. During years 3 and 4, mock tracers will be conducted in EDs, selective inpatient units (including pediatrics), and selective outpatient practices (including pediatrics) across the five institutions - 15 tracers at each for a total of 75.

Aim 2: Co-produce a frame of themes underlying diagnostic overshadowing to develop algorithms to identify patients with specific disabilities at risk of DE along with EHR decision supports and prompts/alerts on specific issues. Educational materials on the algorithms and the EHR decision supports and prompts/alerts along with case studies to educate providers on DE and EHR materials will be developed.

Data analysis plan

At Year 5 compared to Year 1, CPT E/M code usage with the five identified groups of people with disabilities (quantitative) will be conducted to evaluate whether coding usage changed for PWD after implementation of algorithms to identify people with disabilities at risk of diagnostic overshadowing/diagnostic error along with EHR decision supports and prompts/alerts on specific issues. Binary or ordinal pre-post ANCOVA regression analysis will be conducted for each setting (ED, inpatient, outpatient, preventive care) either separately or as clusters with the pretreatment outcome and post-treatment outcomes as binary or ordinal percentages. Independent variables will be patient demographics of age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, type of insurance, and applicable severity index, and the 10 chart review issues listed above either separately or as composites. Developed education programs will be evaluated through 1) pre- and post- knowledge checks of usage and 2) descriptive data on use of specific EHR decision supports as independent t tests and as regression analysis with independent variables being setting, gender, race/ethnicity, age range, type of provider, and setting of the provider (ED, inpatient, outpatient, preventive care). Pre-post time to diagnostic evaluation using ANCOVA to evaluate for differences will be conducted for 2-3 issues still yet to be determined with demographic characteristics of patients with disabilities and provider characteristics as independent variables. Interaction effects will be evaluated. For quantitative measures (CPT E/M code usage analysis, knowledge checks, descriptive data on use of EHR decision supports and prompts/alert), ANCOVA will be used to probe for interaction effects using pre-test measures and independent variables from Year one.

Final mock tracers and analysis will be conducted at the end of Year 4 and in Year 5. Qualitative analysis of review notes compared to notes before intervention will be conducted.

• Study Type: Observational
• Enrollment (Estimated): 120000

Contacts and Locations

• Study Contact: Name: Director Research Affairs; Phone Number: 312-942-3554; Email: Jennifer_Garcia@rush.edu
• Study Contact Backup: Name: Tricia J Johnson, PhD; Phone Number: 312-942-7107; Email: Tricia_J_Johnson@rush.edu

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60612
      • Recruiting
      • Rush University Medical Center
      • Contact: Sarah H Ailey, PhD RN; Phone Number: 3129423383; Email: Sarah_H_Ailey@rush.edu
      • Contact: Director Sponsored programs, CRA; Phone Number: 312 942-3554

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Cases:

• Cases without disabilities are patients 3 - 89 years old without the following principal and secondary diagnoses (using associated ICD-10 codes):
• Cases with disabilities are patients aged 3 - 89 years old with the following

Principal and secondary diagnoses (using associated ICD-10 codes):

• Mobility impairments: Spinal cord diseases, Spinal cord injuries, Injury to spinal cord nerves, Multiple sclerosis, Cerebral palsy, Dependence on enabling machines or devices, Need for caregiver related to mobility impairments
• Severe Vision impairments
• Severe Hearing impairments
• Mental health: Person history mental and behavioral disorders, Major Depression, Severe bipolar disorder, Severe schizophrenia, Paranoia, Psychosis
• Intellectual Disabilities
• Autism

Description

Inclusion Criteria:

• Patients aged 3-89 who received billed charges

Exclusion Criteria:

• Patients under age 3 or over age 89.
• Patients with secondary diagnosis of dementia as the population is already known to be at increased risk of diagnostic error

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with disabilities; Cases with disabilities are patients aged 3 - 89 years old with one or more of 26 diagnoses prone to error and and have the following secondary (or primary) diagnoses (using associated ICD-10 codes) Mobility impairments: Spinal cord diseases, Spinal cord injuries, Injury to spinal cord nerves, Multiple sclerosis, Cerebral palsy, Dependence on enabling machines or devices, Need for caregiver related to mobility impairments Severe Vision impairments: Severe Hearing impairments: Mental health: Person history mental and behavioral disorders, Major Depression, Severe bipolar disorder, Severe schizophrenia, Paranoia, Psychosis Intellectual Disabilities: Autism	Behavioral: Electonic health record prompts with education; . A baseline description of patients aged ≥ 3 to 89 years old with one or more of 26 diagnoses prone to error was developed to compare cases of patients with specific disabilities (major mobility impairments, severe mental health concerns, severe visual impairments, severe hearing loss, and IDD) versus cases of patients without the specific disabilities who have these diagnoses.; Initial manual chart reviews on PWDs with specific diagnoses of sepsis and metastatic breast cancer with two of the disability groups, patients with intellectual disabilities and/or autism and with severe mental illness were done.; We analyzed mammogram usage and factors related to usage among women ≥ 40 years old with IDD and with severe mental health issues 3) We are working on algorithms to identify providers whose female patients age > 40 years old to encourage outreach and to identify the patients needing mammograms for outreach. Developing educational materials for both providers and patients.
Patients without disabilities; Patients without disabilities are patients age 3-89 with one or more of 26 diagnoses prone to error and without the following secondary (or primary) diagnoses: Mobility impairments: Spinal cord diseases, Spinal cord injuries, Injury to spinal cord nerves, Multiple sclerosis, Cerebral palsy, Dependence on enabling machines or devices, Need for caregiver related to mobility impairments Severe Vision impairments: Severe Hearing impairments: Mental health: Person history mental and behavioral disorders, Major Depression, Severe bipolar disorder, Severe schizophrenia, Paranoia, Psychosis Intellectual Disabilities: Autism	Other: Standard of care; Patients without disabilities will receive standard care related to electronic health record prompts, alerts, and decision supports.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Knowledge questionnaires	Knowledge questionnaires will be developed related to algorithms to detect people with disabilities from 5 specified groups at risk of diagnostic overshadowing, EHR prompts/alerts and decisions supports. Pre and post, the percentage of correct answers will be calculated and compared using ANCOVA.	3.5 years
Descriptive data on use of electronic record (EHR) decision supports and prompts/alerts	After implementation of EHR prompts/alerts and decision supports related to diagnostic error, descriptive data will be collected and analyzed on usage.	1.5, 2.5, and 3.5 years
Complexity distribution of Evaluation and Management (E/M) Current Procedural Technology (CPT) codes	The percentage of each complexity score for Current Procedural Technology (CPT) Evaluation and Management (E/M) codes will be measured by setting (ED, outpatient, inpatient, preventive care) for differences using Fisher'ss exact tests for patients with disabilities (PWD) aged 3-89 years old with specific disabilities (major mobility impairments, mental health concerns, severe visual impairments/ blindness, severe hearing loss/deafness, and IDD) versus patients aged 3-89 years old without the specific disabilities.	4 years
Scores on Safer DX Checklist	The Safer Dx Instrument uses a Likert scale to rate the degree of agreement with statement regarding diagnostic processes. Higher scores may indicate a greater likelihood of a diagnostic error or "missed opportunity" for diagnosis.	4 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mock tracer qualitative analysis	Qualitative analysis of review notes from mock tracers pre-intervention will be compared to notes post intervention.	Years 4 and 5

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Interaction effects pre and post diagnostic processes	Using pre-test measures and independent variables from year one, ANCOVA will be used to measure interaction effects on effects of the intervention. Age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, insurance type, and severity of illness [relevant to site]), and the 10 issues suggested by stakeholders (singly or in composites) will be entered as covariates in ANCOVA and measured for variation by setting in post-test results of CPT E/M code complexity usage analysis, Safer DX Checklist scores, knowledge checks, descriptive data on use of EHR decision supports and prompts/alert.	3.5 years

Collaborators and Investigators

• Sponsor: Rush University Medical Center
• Collaborators: University of Minnesota; St. John Fisher College
• Investigators: Principal Investigator: Sarah H Ailey, PhD RN, Rush University College of Nursing

Publications and helpful links

General Publications

• 163. Fram S. The constant comparative analysis method outside of grounded theory. Qualitative report. 2015. doi: 10.46743/2160-3715/2013.1569. 164. McMillan SS, Kelly F, Sav A, et al. Using the nominal group technique: How to analyse across multiple groups. Health Serv Outcomes Res Method. 2014;14(3):92-108. https://link.springer.com/article/10.1007/s10742-014-0121-1. doi: 10.1007/s10742-014-0121-1. 165. Krueger RA, Casey MA. Focus groups. 5th ed. Thousand Oaks: SAGE Publications, Incorporated; 2014. https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=7106821. Version 5/21/25 Page 63 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 166. Abery B, Ticha R, Bathje M, Ailey S. Participatory planning and decision making: Themes in the education of health professionals in the health of people with intellectual and developmental disabilities. . 167. Reiser RA, Dempsey JV. Trends and issues in instructional design and technology. Fourth edition ed. New York, NY: Pearson; 2018.
• 156. How Rochester regional health is raising health equity in healthcare Rochester Regional Health Web site. https://hive.rochesterregional.org/2023/02/health-equity-rochester. Accessed November 21, 2023. 158. AMA: New 2023 CPT code set includes burden-reducing revisionsAmerican Medical Association Press releases Web site. https://www.ama-assn.org/press-center/press-releases/ama- new-2023-cpt-code-set-includes-burden-reducing-revisions. Updated 2022. Accessed November 21, 2023. 159. Skinner TR, Scott IA, Martin JH. Diagnostic errors in older patients: A systematic review of incidence and potential causes in seven prevalent diseases. International journal of general Version 5/21/25 Page 62 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 medicine. 2016;9(Issue 1):137-146. https://www.ncbi.nlm.nih.gov/pubmed/27284262. doi: 10.2147/IJGM.S96741. 160. Wright N, Anthony D, McArdle K, Ailey S. Case study in tracer methodology: CARE OF PATIENTS WHO ARE BLIND OR VISUALLY IMPAIREDJoint Commission : The Source. 2018;16(8):1. 161. Augustine JJ. Latest data reveal the ED's role as hospital admission gatekeeper. ACEPNow: The Official Voice of Emergency Medicine. 2019. https://www.acepnow.com/article/latest-data- reveal-the-eds-role-as-hospital-admission- gatekeeper/#:~:text=In%202018%2C%20inpatient%20units%20were%20the%20site%20of,70% 20percent%20of%20hospital%20inpatients%20processed%20through%20it.. 162. Miles MB. Qualitative data analysis. Edition 3 ed. Los Angeles: Sage; 2014. http://www.econis.eu/PPNSET?PPN=1605448125.
• 6692. doi: 10.1352/1934-9556-61.4.326. 148. Stancliffe RJ, Pettingell SL, Bershadsky J, Houseworth J, Tichá R. Community participation and staying home if you want: US adults with intellectual and developmental disabilities. Journal of Applied Research in Intellectual Disabilities. 2022;35(5):1199-1207. doi: 10.1111/jar.13014. 149. Pettingell SL, Houseworth J, Tichá R, Kramme JED, Hewitt AS. Incentives, wages, and retention among direct support professionals: National core indicators staff stability survey. Intellectual and developmental disabilities. 2022;60(2):113-127. doi: 10.1352/1934-9556-60.2.113. 150. Tichá R, Hewitt A, Nord D, Larson S. System and individual outcomes and their predictors in services and support for people with IDD. Intellect Dev Disabil. 2013;51(5):298-315. doi: 10.1352/1934-9556-51.5.298. 151. Qian X, Tichá R, Stancliffe R. Contextual factors associated with implementing active support in community group homes in the united states: A qualitative investigation. J Policy Pract Intellect Disabil. 2017;14(4):332-340. doi: 10.1111/jppi.12204. Version 5/21/25 Page 61 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 152. RTC/OM briefs. Rehabilitation Research and Training Center on HCBS Outcome Measurement Web site. https://publications.ici.umn.edu/rtcom/briefs/brief-one-involving- stakeholders-to-address-challenges-in-hcbs-mesure-development. 153. IDD health equityIDD Health Equity Web site. https://iddhealthequity.org/. 154. Among the nation's best. RUSH System for Health Web site. https://www.rush.edu/. 155. Ansell DA, Oliver-Hightower D, Goodman LJ, Lateef OB, Johnson TJ. Health equity as a system strategy: The rush university medical center framework. NEJM catalyst innovations in care delivery. 2021;2(5). doi: 10.1056/CAT.20.0674.
• 143. Stancliffe RJ, Tichá R, Pettingell SL, Houseworth J, Bershadsky J. Current services and outcomes of formerly institutionalised and never-institutionalised US adults with intellectual and developmental disabilities: A propensity score matching analysis. Journal of applied research in intellectual disabilities. 2023;36(4):859-870. https://onlinelibrary.wiley.com/doi/abs/10.1111/jar.13103. doi: 10.1111/jar.13103. 144. Cherry E, Stancliffe RJ, Emerson E, Tichá R. Policy implications, eligibility, and demographic characteristics of people with intellectual disability who access self-directed funding in the united states. Intellectual and developmental disabilities. 2021;59(2):123-140. http://eric.ed.gov/ERICWebPortal/detail?accno=EJ1291832. doi: 10.1352/1934-9556-59.2.123. 145. Stancliffe RJ, Pettingell SL, Tichá R, Houseworth J. Mothers and fathers with intellectual and developmental disabilities who use US disability services: Prevalence and living arrangements. Journal of intellectual disability research. 2022;66(3):297-305. https://onlinelibrary.wiley.com/doi/abs/10.1111/jir.12912. doi: 10.1111/jir.12912. 146. Stancliffe RJ, Tichá R, Larson SA, Hewitt AS, Nord D. Responsiveness to self-report interview questions by adults with intellectual and developmental disability. Intellectual and developmental disabilities. 2015;53(3):163-181. https://www.ncbi.nlm.nih.gov/pubmed/26107851. doi: 10.1352/1934-9556-53.3.163. 147. Stancliffe RJ, Pettingell SL, Houseworth J, Tichá R. Participation and companions for socially inclusive community activities by U.S. adults with intellectual and developmental disabilities. Intellectual and developmental disabilities. 2023;61(4):326-344. https://www.ncbi.nlm.nih.gov/pubmed/37536692. doi: 10.1352/1934-9556-61.4.326.
• 135. Bernadette Mazurek Melnyk, PhD, APRN-CNP, FAANP, FNAP, FAAN, Pamela Lusk, DNP, RN, PMHNP-BC, FAANP, FAAN / Bernadette Mazurek Melnyk, PhD, APRN-CNP, FAANP, FNAP, FAAN, Pamela Lusk, DNP, RN, PMHNP-BC, FAANP, FAAN. A practical guide to child and adolescent mental health screening, evidence-based assessment, intervention, https://www.perlego.com/book/2633732/a-practical-guide-to-child-and-adolescent-mental- health-screening-evidence-based-assessment-intervention-and-health-promotion-pdf. 136. Wehmeyer ML, Abery BH. Self-determination and choice. Intellect Dev Disabil. 2013;51(5):399-411. 137. Tichá R, Abery B, Šiška J. Editorial: Improving the quality of outcome measurement for adults with disabilities receiving community-based services. Frontiers in rehabilitation sciences. 2023;4:1163522. https://www.ncbi.nlm.nih.gov/pubmed/37064597. doi: 10.3389/fresc.2023.1163522. 138. Roberts MA, Abery BH. A person-centered approach to home and community-based services outcome measurement. Frontiers in rehabilitation sciences. 2023;4:1056530. https://www.ncbi.nlm.nih.gov/pubmed/36817716. doi: 10.3389/fresc.2023.1056530. 139. Wehmeyer ML, Abery BH. Self-determination and choice. Intellectual and developmental disabilities. 2013;51(5):399-411. http://eric.ed.gov/ERICWebPortal/detail?accno=EJ1015845. doi: 10.1352/1934-9556-51.5.399. 140. Stancliffe RJ, Wehmeyer ML, Shogren KA, Abery BH. Choice, preference, and disabilityhttps://doi.org/10.1007/978-3-030-35683-5. Updated 2020. 141. Abery B, Anderson LL. Preference, choice, and self-determination in the healthcare context. In: Stancliffe RJ, Wehmeyer MI, Shagrean KA, Abery BH, eds. Choice, preference, and disability: Promoting self-determination across the lifespan; 2020:155-175. 142. Houseworth J, Stancliffe RJ, Tichá R. Association of state-level and individual-level factors with choice making of individuals with intellectual and developmental disabilities. Research in developmental disabilities. 2018;83:77-90 https://dx.doi.org
• 126. Weinberg DB, Cooney-Miner D, Perloff JN. Analyzing the relationship between nursing education and patient outcomes. J NURS REGUL. 2012;3(2):4-10. doi: 10.1016/S2155-8256(15)30212-X. 127. Weinberg DB, Cooney-Miner D, Perloff JN, Babington L, Avgar AC. Building collaborative capacity: Promoting interdisciplinary teamwork in the absence of formal teams. Med Care. 2011;49(8):716-723. doi: 10.1097/MLR.0b013e318215da3f. 128. Dobrowolska B, McGonagle I, Jackson C, et al. Clinical practice models in nursing education: Implication for students' mobility. Int Nurs Rev. 2015;62(1):36-46. doi: 10.1111/inr.12162. 129. Dobrowolska B, McGonagle I, Kane R, et al. Patterns of clinical mentorship in undergraduate nurse education: A comparative case analysis of eleven EU and non-EU countries. Nurse Educ Today. 2016;36:44-52. doi: 10.1016/j.nedt.2015.07.010. 130. Weinberg DB, Cooney-Miner D, Perloff JN, Bourgoin M. The gap between education preferences and hiring practices. Nurs Manage. 2011;42(9):23-28. doi: 10.1097/01.NUMA.0000399676.35805.f8. 131. Weinberg DB, Avgar AC, Sugrue NM, Cooney-Miner D. The importance of a high-performance work environment in hospitals. Health Serv Res. 2013;48(1):319-332. doi: 10.1111/j.1475-6773.2012.01438.x. 132. Cooney Miner D. Transforming the nursing workforce in new york: The value of baccalaureate preparation in nursing. J NY STATE NURSES ASSOC. 2013;43(2):17-20. 133. Golisano institute administrationSt. John Fisher University Web site. https://www.sjf.edu/institutes/golisano-institute/faculty-and-staff/. 134. Melnyk BM, Brown HE, Jones DC, Kreipe R, Novak J. Improving the mental/psychosocial health of US children and adolescents: Outcomes and implementation strategies from the national KySS summit. Journal of pediatric health care. 2003;17(6 Suppl):1. doi: 10.1016/j.pedhc.2003.08.002.
• developmental disabilities . 2022. 118. Ailey SH, Johnson TJ, Fogg L, Friese TR. Factors related to complications among adult patients with intellectual disabilities hospitalized at an academic medical center. Intellect Dev Disabil. 2015;53(2):114-119. doi: 10.1352/1934-9556-53.2.114 [doi]. 119. Ailey SH, Johnson T, Fogg L, Friese TR. Hospitalizations of adults with intellectual disability in academic medical centers. Intellect Dev Disabil. 2014;52(3):187-192. doi: 10.1352/1934-9556-52.3.187 Version 5/21/25 Page 55 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 120. Ailey SH, Johnson TJ, Cabrera A. Evaluation of factors related to prolonged lengths of stay for patients with autism with or without intellectual disability. J Psychosoc Nurs Ment Health Serv. 2019:1-6. doi: 10.3928/02793695-20190205-01 [doi]. 121. Wirtz J, Ailey SH, Hohmann S, Johnson T. Patient outcomes associated with tailored hospital programs for intellectual disabilities. The American journal of managed care. 2020;26(3):e84-e90. https://www.ncbi.nlm.nih.gov/pubmed/32181620. doi: 10.37765/ajmc.2020.42640. 122. Chakravarthy V, Ryan MJ, Jaffer A, et al. Efficacy of a transition clinic on hospital readmissions. Am J Med. 2018;131(2):178-184.e1. doi: S0002-9343(17)30934-8 [pii]. 123. Altfeld SJ, Shier GE, Rooney M, et al. Effects of an enhanced discharge planning intervention for hospitalized older adults: A randomized trial. Gerontologist. 2013;53(3):430- 440. doi: 10.1093/geront/gns109 [doi]. 124. Johnson TJ, Patel AL, Jegier BJ, et al. Cost of morbidities in very low birth weight infants. J Pediatr. 2013;162(2):243-249.e1. doi: 10.1016/j.jpeds.2012.07.013. 125. Ailey SH, Johnson TJ, Cabrera A. Evaluation of factors related to prolonged lengths of stay for patients with autism with or without intellectual disability. J Psychosoc Nurs Ment Health Serv. 2019;57(7):17-22. doi: 10.3928/02793695-20190205-01
• 107. Ailey SH, Hart R. Hospital program for working with adult clients with intellectual and developmental disabilities. Intellect Dev Disabil. 2010;48(2):145-147. 108. Ailey SH, Brown PJ, Ridge CM. Improving hospital care of patients with intellectual and developmental disabilities. Disabil Health J. 2017;10(2):169-172. 109. Ailey SH, Johnson T, Fogg L, Friese TR. Hospitalizations of adults with intellectual disability in academic medical centers. Intellect Dev Disabil. 2014;52(3):187-192. 110. Ailey SH, Johnson TJ, Fogg L, Friese TR. Factors related to complications among adult patients with intellectual disabilities hospitalized at an academic medical center. Intellect Dev Disabil. 2015;53(2):114-119. 111. Friese T, Ailey S. Specific standards of care for adults with intellectual disabilities. Nurs Manag (Harrow). 2015;22(1):32-37. doi: 10.7748/nm.22.1.32.e1296 [doi]. 112. Ailey S.H., Christopher BA, Ramson, K., Schmidt, N., & Lapin, A. Tracer methodology 101: Using mock tracers to evaluate care of patients with intellectual disabilities, part 2. . The Source. 2016;14(1):4-6. 113. Ailey S.H., Christopher BA, Ramson, K., Schmidt, N., & Lapin, A. Tracer methodology 101: Using mock tracers to evaluate care of patients with intellectual disabilities, part 1. The Source. 2015;13(12):4-6. 114. Wright N, Anthony D, McArdle KJ, Ailey SH. Case study in tracer methodology: Care of patients who are blind or visually impaired. Joint Commission: The Source. ;16(8):4-6. 115. Ailey SH, Smeltzer S, Marks B, et al. Partnering to transform health outcomes with persons with intellectual disabilities (PATH-PWIDD) year three. 2023. 116. Ailey SH, Brown H. Will proposed ACGME requirements have consequences for kids with autism . 2023. 117. Ailey SH, Brown H. Hospitals can be dangerous places for people with intellectual and developmental disabilities . 2022.
• 97. Diaz T, Strong KL, Cao B, et al. A call for standardised age-disaggregated health data. The Lancet. Healthy longevity. 2021;2(7):e436-e443. https://dx.doi.org/10.1016/S2666- 7568(21)00115-X. doi: 10.1016/S2666-7568(21)00115-X. 98. Cameron AC, Trivedi PK. Microeconometrics using stata. volume II: Nonlinear models and causal inference methods. In: Multinomial models. second edition. College Station TX: Stata Press; 2022. 99. Whitaker RG, Sperber N, Baumgartner M, et al. Coincidence analysis: A new method for causal inference in implementation science. Implementation Science. 2020;15(1):1-108. https://www.ncbi.nlm.nih.gov/pubmed/33308250. doi: 10.1186/s13012-020-01070-3. Version 5/21/25 Page 52 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 100. Baumgartner M. Regularity theories reassessed. Philosophia. 2008;36(3):327-354. https://link.springer.com/article/10.1007/s11406-007-9114-4. doi: 10.1007/s11406-007-9114-4. 101. Baumgartner M, Falk C. Boolean difference-making: A modern regularity theory of causation. The British journal for the philosophy of science. 2023;74(1):171-197. https://www.journals.uchicago.edu/doi/abs/10.1093/bjps/axz047. doi: 10.1093/bjps/axz047. 102. Wolfe L, Chisolm MS, Bohsali F. Clinically excellent use of the electronic health record: Review. JMIR Human Factors. 2018;5(4):e10426. https://10.2196/10426. doi: 10.2196/10426. 103. Kania J, Kramer M. Collective impact. Stanford Social Innovation Review. 2011;9(1). https://ssir.org/articles/entry/collective_impact. 105. Ailey SH, Brown P, Friese TR, Dugan S. Building a culture of inclusion: Disability as opportunity for organizational growth and improving patient care. J Nurs Adm. 2016;46(1):9-11. doi: 10.1097/NNA.0000000000000286 [doi]. 106. Society for nurses with disabilities: National organization of nurses with disabilitiesNurses with disabilities Web site. https://www.nurseswithdisabilities.org/2011/02/national-organization- of-nurses-with.html.
• 88. Turakhia P, Combs B. Using principles of co-production to improve patient care and enhance value. AMA J Ethics. 2017;19(11):1125-1131. doi: journalofethics.2017.19.11.pfor1-1711 89. Rycroft-Malone J, Burton CR, Bucknall T, Graham ID, Hutchinson AM, Stacey D. Collaboration and co-production of knowledge in healthcare: Opportunities and challenges. Int J Health Policy Manag. 2016;5(4):221-223. doi: 10.15171/ijhpm.2016.08 [doi]. 90. Participation of organisations of persons with disabilities in development programmes and policies: IDA global survey initial reportInternational Disability Alliance. 2019. 91. Rycroft-Malone J, Burton CR, Wilkinson J, et al. Collective action for implementation: A realist evaluation of organisational collaboration in healthcare. Implement Sci. 2016;11:17-z. doi: 10.1186/s13012-016-0380-z [doi]. 92. Cox R, Molineux M, Kendall M, Tanner B, Miller E. Co-produced capability framework for successful patient and staff partnerships in healthcare quality improvement: Results of a scoping review. BMJ Qual Saf. 2022;31(2):134-146. doi: 10.1136/bmjqs-2020-012729 [doi]. 93. Lewis DR, Johnson DR, Braddock DL. Participatory evaluation for special education and rehabilitation. Washington DC: American Association on Mental Retardation; 2000. Version 5/21/25 Page 51 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 94. Lenette C. Participatory action research. New York, NY: Oxford University Press; 2022. https://www.gbv.de/dms/bowker/toc/9780197644966.pdf. 10.1093/oso/9780197512456.001.0001. 95. Wehmeyer M. Decision making and self-determination. In: Khemka I, Hickson L, eds Decision making by individuals with intellectual and developmental disabilities. positive psychology and disability series. Springer; 2021. 96. Stancliffe RJ, Abery BH, Smith J. Personal control and the ecology of community living settings: Beyond living-unit size and type. American journal of mental retardation. 2000;105(6):431-454. http://eric.ed.gov
• 82. Tsai CC, Starren J. Patient participation in electronic medical records. JAMA : the journal of the American Medical Association. 2001;285(13):1765. http://dx.doi.org/10.1001/jama.285.13.1765-JMS0404-3-1. doi: 10.1001/jama.285.13.1765- JMS0404-3-1. 83. Molina MF, Cash RE, Carreras-Tartak J, et al. Applying crisis standards of care to critically ill patients during the COVID-19 pandemic: Does race/ethnicity affect triage scoring? Journal of the American College of Emergency Physicians Open. 2021;2(4):e12502-n/a. https://onlinelibrary.wiley.com/doi/abs/10.1002/emp2.12502. doi: 10.1002/emp2.12502. 84. Meyer A, Murphy D, Al-Mutairi A, et al. Electronic detection of delayed test result follow- up in patients with hypothyroidism. JGIM: Journal of General Internal Medicine. 2017;32(7):753-759. https://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=123732180&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1007/s11606-017-3988-z. 85. Medford-Davis L, Park E, Shlamovitz G, et al. Diagnostic errors related to acute abdominal pain in the emergency department. Emerg Med J. 2016;33(4):253-259. https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=114060423&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1136/emermed-2015-204754. 86. Murphy DR, Wu L, Thomas EJ, Forjuoh SN, Meyer AND, Singh H. Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: A cluster randomized controlled trial. J Clin Oncol. 2015;33(31):3560-3567. https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110557165&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1200/JCO.2015.61.1301. 87. Wood DS, Tracey TJG. A brief feedback intervention for diagnostic overshadowing. Train Educ Prof Psychol. 2009;3(4):218-225. Version 5/21/25 Page 50 of 64ORA: 23111505-IRB01 https://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-21175- 004&authtype=sso&custid=cls59&site=ehost-live. doi: 10.1037/a0016577.
• NEJM197301252880406. doi: 10.1056/NEJM197301252880406. 75. Bouchard C, Jean O. Tracer methodology: An appropriate tool for assessing compliance with accreditation standards? The International journal of health planning and management. 2017;32(4):e299-e315. https://onlinelibrary.wiley.com/doi/abs/10.1002/hpm.2376. doi: 10.1002/hpm.2376. 76. Siewert B. The joint commission ever-readiness: Understanding tracer methodology. Current problems in diagnostic radiology. 2018;47(3):131-135. https://dx.doi.org/10.1067/j.cpradiol.2017.05.002. doi: 10.1067/j.cpradiol.2017.05.002. Version 5/21/25 Page 48 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 77. The Joint Commission. How to conduct a mock tracer. 78. Zazove P, McKee M, Schleicher L, et al. To act or not to act: Responses to electronic health record prompts by family medicine clinicians. Journal of the American Medical Informatics Association : JAMIA. 2017;24(2):275-280. https://www.ncbi.nlm.nih.gov/pubmed/28158766. doi: 10.1093/jamia/ocw178. 79. Dixit RA, Boxley CL, Samuel S, Mohan V, Ratwani RM, Gold JA. Electronic health record use issues and diagnostic error: A scoping review and framework. Journal of patient safety. 2023;19(1):e25-e30. https://www.ncbi.nlm.nih.gov/pubmed/36538341. doi: 10.1097/PTS.0000000000001081. 80. Krevat SA, Samuel S, Boxley C, et al. Identifying electronic health record contributions to diagnostic error in ambulatory settings through legal claims analysis. JAMA Network Open. 2023;6(4):e238399. http://dx.doi.org/10.1001/jamanetworkopen.2023.8399. doi: 10.1001/jamanetworkopen.2023.8399. 81. Nelson H. Epic EHR integration aims to boost clinical decision support, CGP . EHR Intelligence Web site. https://www.ehrintelligence.com/news/epic-ehr-integration-aims-to-boost-clinical-decision-support-cgp.
• 68. Simon VC, Tucker NJ, Balabanova A, Parry JA. The accuracy of hip fracture data entered into the national surgical quality improvement program (NSQIP) database. Eur J Orthop Surg Traumatol. 2023;33(5):1691-1695. https://link.springer.com/article/10.1007/s00590-022-03341- 9. doi: 10.1007/s00590-022-03341-9. 69. Giardina TD, Hunte H, Hill MA, Heimlich SL, Singh H, Smith KM. Defining diagnostic error: A scoping review to assess the impact of the national academies' report improving diagnosis in health care. Journal of patient safety. 2022;18(8):770-778. https://www.ncbi.nlm.nih.gov/pubmed/35405723. doi: 10.1097/PTS.0000000000000999. 70. American Medical Association. Evaluation and management (E/M) coding. Version 5/21/25 Page 47 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 71. Chen K, Zhang C, Jackson HB. Relative billing complexity of in-person versus telehealth outpatient encounters. Journal of evaluation in clinical practice. 2023;29(6):887-892. https://onlinelibrary.wiley.com/doi/abs/10.1111/jep.13905. doi: 10.1111/jep.13905. 72. Bhardwaj A. The impact of training on documentation of intellectual and developmental disabilities as a secondary condition: Health systems management master's project . [Masters]. Rush University College of Health Sciences; 2021. 73. Bilotta I, Tonidandel S, Liaw WR, et al. Examining linguistic differences in electronic health records for diverse patients with diabetes: Natural language processing analysis. JMIR medical informatics. 2024;12:e50428. https://www.ncbi.nlm.nih.gov/pubmed/38787295. doi: 10.2196/50428. 74. Kessner DM, Kalk CE, Singer J. Assessing health quality-the case for tracers. The New England journal of medicine. 1973;288(4):189-194. http://dx.doi.org/10.1056/NEJM197301252880406. doi: 10.1056/NEJM197301252880406.
• 61. Singh H, Mushtaq U, Marinez A, et al. Developing the safer dx checklist of ten safety recommendations for health care organizations to address diagnostic errors. Joint Commission journal on quality and patient safety. 2022;48(11):581-590. https://dx.doi.org/10.1016/j.jcjq.2022.08.003. doi: 10.1016/j.jcjq.2022.08.003. 62. Singh H, Khanna A, Spitzmueller C, Meyer AND. Recommendations for using the revised safer dx instrument to help measure and improve diagnostic safety. Diagnosis. 2019;6(4):315- 323. http://www.degruyter.com/doi/10.1515/dx-2019-0012. doi: 10.1515/dx-2019-0012. 63. Kapadia P, Zimolzak AJ, Upadhyay DK, et al. Development and implementation of a digital quality measure of emergency cancer diagnosis. Journal of clinical oncology. 2024;42(21):2506- 2515. https://ovidsp.ovid.com/ovidweb.cgi?T=JS&NEWS=n&CSC=Y&PAGE=fulltext&D=ovft&DO =10.1200/JCO.23.01523. doi: 10.1200/JCO.23.01523. Version 5/21/25 Page 46 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 64. Al-Mutairi A, Meyer AND, Thomas EJ, et al. Accuracy of the safer dx instrument to identify diagnostic errors in primary care. J GEN INTERN MED. 2016;31(6):602-608. https://link.springer.com/article/10.1007/s11606-016-3601-x. doi: 10.1007/s11606-016-3601-x. 65. Hirsch JA, Leslie-Mazwi TM, Nicola GN, et al. Current procedural terminology; a primer. Journal of NeuroInterventional Surgery. 2015;7(4):309-312. http://dx.doi.org/10.1136/neurintsurg-2014-011156. doi: 10.1136/neurintsurg-2014-011156. 66. Dotson P. CPT ® codes: What are they, why are they necessary, and how are they developed? Advances in wound care (New Rochelle, N.Y.). 2013;2(10):583-587. https://www.ncbi.nlm.nih.gov/pubmed/24761332. doi: 10.1089/wound.2013.0483. 67. CPT® overview and code approval American Medical Association. 2023.
• 45. Wislocki KE, Zalta AK. Assessing the existence of trauma-related diagnostic overshadowing in adult populations. Psychological trauma. 2023. https://www.ncbi.nlm.nih.gov/pubmed/37053403. doi: 10.1037/tra0001484. Version 5/21/25 Page 43 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 46. Kostiuk M, Burns B. Trauma assessment. National Library of Medicine Web site. https://www.ncbi.nlm.nih.gov/books/NBK555913/. Updated 2023. 47. McNally P, Taggart L, Shevlin M. Trauma experiences of people with an intellectual disability and their implications: A scoping review. Journal of applied research in intellectual disabilities. 2021;34(4):927-949. https://onlinelibrary.wiley.com/doi/abs/10.1111/jar.12872. doi: 10.1111/jar.12872. 48. Forkey H, Szilagyi M, Kelly ET, Duffee J. Trauma-informed care. Pediatrics (Evanston). 2021;148(2):1. https://www.ncbi.nlm.nih.gov/pubmed/34312292. doi: 10.1542/peds.2021- 052580. 49. Lin P, Daly AT, Olchanski N, et al. Dementia diagnosis disparities by race and ethnicity. Medical care. 2021;59(8):679-686. https://search.proquest.com/docview/2549684438. doi: 10.1097/MLR.0000000000001577. 50. Gallegos A, Dudovitz R, Biely C, et al. Racial disparities in developmental delay diagnosis and services received in early childhood. Academic pediatrics. 2021;21(7):1230-1238. https://dx.doi.org/10.1016/j.acap.2021.05.008. doi: 10.1016/j.acap.2021.05.008. 51. Goyal MK, Chamberlain JM, Webb M, et al. Racial and ethnic disparities in the delayed diagnosis of appendicitis among children. Academic emergency medicine. 2021;28(9):949-956. https://onlinelibrary.wiley.com/doi/abs/10.1111/acem.14142. doi: 10.1111/acem.14142. 52. Cho HL. Can intersectionality help lead to more accurate diagnosis? American journal of bioethics. 2019;19(2):37-39. Version 5/21/25 Page 44 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 https://www.tandfonline.com/doi/abs/10.1080/15265161.2018.1557279. doi: 10.1080/15265161.2
• 45. Wislocki KE, Zalta AK. Assessing the existence of trauma-related diagnostic overshadowing in adult populations. Psychological trauma. 2023. https://www.ncbi.nlm.nih.gov/pubmed/37053403. doi: 10.1037/tra0001484. Version 5/21/25 Page 43 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 46. Kostiuk M, Burns B. Trauma assessment. National Library of Medicine Web site. https://www.ncbi.nlm.nih.gov/books/NBK555913/. Updated 2023. 47. McNally P, Taggart L, Shevlin M. Trauma experiences of people with an intellectual disability and their implications: A scoping review. Journal of applied research in intellectual disabilities. 2021;34(4):927-949. https://onlinelibrary.wiley.com/doi/abs/10.1111/jar.12872. doi: 10.1111/jar.12872. 48. Forkey H, Szilagyi M, Kelly ET, Duffee J. Trauma-informed care. Pediatrics (Evanston). 2021;148(2):1. https://www.ncbi.nlm.nih.gov/pubmed/34312292. doi: 10.1542/peds.2021- 052580. 49. Lin P, Daly AT, Olchanski N, et al. Dementia diagnosis disparities by race and ethnicity. Medical care. 2021;59(8):679-686. https://search.proquest.com/docview/2549684438. doi: 10.1097/MLR.0000000000001577. 50. Gallegos A, Dudovitz R, Biely C, et al. Racial disparities in developmental delay diagnosis and services received in early childhood. Academic pediatrics. 2021;21(7):1230-1238. https://dx.doi.org/10.1016/j.acap.2021.05.008. doi: 10.1016/j.acap.2021.05.008. 51. Goyal MK, Chamberlain JM, Webb M, et al. Racial and ethnic disparities in the delayed diagnosis of appendicitis among children. Academic emergency medicine. 2021;28(9):949-956. https://onlinelibrary.wiley.com/doi/abs/10.1111/acem.14142. doi: 10.1111/acem.14142. 52. Cho HL. Can intersectionality help lead to more accurate diagnosis? American journal of bioethics. 2019;19(2):37-39. https://www.tandfonline.com/doi/abs/10.1080/15265161.2018.1557279. doi: 10.1080/15265161.2
• 38. Bewick T, Simmonds M, Chikhani M, Meyer J, Lim WS. Pneumonia in the context of severe sepsis: A significant diagnostic problem. The European respiratory journal. 2008;32(5):1417- 1418. https://www.ncbi.nlm.nih.gov/pubmed/18978148. doi: 10.1183/09031936.00104808. 39. New chronic pain findings reported from national institute on aging (the silent epidemic of chronic pain in older adults)Mental Health Weekly Digest. Jul 15 2019:381. Version 5/21/25 Page 42 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 40. Rikard SM, Strahan AE, Schmit KM, Guy J, Gery P. Chronic pain among adults - united states, 2019-2021. MMWR. Morbidity and mortality weekly report. 2023;72(15):379-385. https://www.ncbi.nlm.nih.gov/pubmed/37053114. doi: 10.15585/mmwr.mm7215a1. 41. Flentje KM, Knight CL, Stromfeldt I, Chakrabarti A, Friedman ND. Recording patient bodyweight in hospitals: Are we doing well enough? Internal medicine journal. 2018;48(2):124- 128. https://onlinelibrary.wiley.com/doi/abs/10.1111/imj.13519. doi: 10.1111/imj.13519. 42. Yasinski E. For many disabled patients, the doctor is often not in. undark Web site. https://undark.org/2022/11/07/for-many-disabled-patients-the-doctor-is-often-not-in/. Updated 2022. Accessed November 21, 2023. 43. Landes SD, Stevens JD, Turk MA. Postmortem diagnostic overshadowing: Reporting cerebral palsy on death certificates. Journal of health and social behavior. 2022;63(4):525-542. https://journals.sagepub.com/doi/full/10.1177/00221465221078313. doi: 10.1177/00221465221078313. 44. Landes SD, Turk MA, Finan JM. Factors associated with the reporting of down syndrome as the underlying cause of death on US death certificates. Journal of intellectual disability research. 2022;66(5):454-470. https://onlinelibrary.wiley.com/doi/abs/10.1111/jir.12926. doi: 10.1111/jir.12926.
• 31. Epner, Pauk. Diagnostic error: Common, catastrophic, and costly . 2021. https://thepathologist.com/outside-the-lab/diagnostic-error-common-catastrophic-and-costly. 32. Kim Y, Shin DW, Kim HW, et al. Disparities in gastric cancer screening among people with disabilities: A national registry-linkage study in south korea. Gastric Cancer. 2020;23(3):497- 509. https://link.springer.com/article/10.1007/s10120-019-01017-8. doi: 10.1007/s10120-019- 01017-8. 33. McMahon M, McCallion P, McCarron M. An invisible population: Late-stage cancer diagnosis for people with intellectual or developmental disability. Cancer. 2024;130(5):668-670. Version 5/21/25 Page 41 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175446967&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1002/cncr.35132. 34. The joint commission issues sentinel event alert on diagnostic overshadowingTargeted News Service. Jun 24 2022. Available from: https://search.proquest.com/docview/2680211091.. 35. Boonman AJ, Cuypers M, Leusink GL, Naaldenberg J, Bloemendal HJ. Cancer treatment and decision making in individuals with intellectual disabilities: A scoping literature review. The lancet oncology. 2022;23(4):e174-e183. https://www.ncbi.nlm.nih.gov/pubmed/35358466. doi: 10.1016/S1470-2045(21)00694-X. 36. Newman-Toker DE, Wang Z, Zhu Y, et al. Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: Toward a national incidence estimate using the "big three". Diagnosis (Berlin, Germany). 2021;8(1):67-84. http://www.degruyter.com/doi/10.1515/dx-2019-0104. doi: 10.1515/dx-2019-0104. 37. Hanna TP, King WD, Thibodeau S, et al. Mortality due to cancer treatment delay: Systematic review and meta-analysis. BMJ (Online). 2020;371:m4087. http://dx.doi.org/10.1136/bmj.m4087. doi: 10.1136/bmj.m4087
• 22. Bogart KR, Dunn DS. Ableism special issue introduction. Journal of social issues. 2019;75(3):650-664. https://onlinelibrary.wiley.com/doi/abs/10.1111/josi.12354. doi: 10.1111/josi.12354. 23. Friedman C. Mapping ableism: A two-dimensional model of explicit and implicit disability attitudes. Canadian journal of disability studies. 2019;8(3):95-120. doi: 10.15353/cjds.v8i3.509. 24. VanPuymbrouck L, Friedman C, Feldner H. Explicit and implicit disability attitudes of healthcare providers. Rehabilitation psychology. 2020;65(2):101-112. http://psycnet.apa.org/journals/rep/65/2/101. doi: 10.1037/rep0000317. 25. Valdez RS, Swenor BK. Structural ableism - essential steps for abolishing disability injustice. The New England journal of medicine. 2023;388(20):1827-1829. https://nejm.org/doi/full/10.1056/NEJMp2302561. doi: 10.1056/NEJMp2302561. 26. Iezzoni LI, Rao SR, Ressalam J, et al. Physicians' perceptions of people with disability and their health care . Health affairs (Millwood, Va.). 2021;40(2):297-12. https://www.ncbi.nlm.nih.gov/pubmed/33523739. doi: 10.1377/hlthaff.2020.01452. Version 5/21/25 Page 40 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 27. McMahon MA, Dluhy NM. Ambiguity within nursing practice: An evolutionary concept analysis. Research and theory for nursing practice. 2017;31(1):56-74. https://dx.doi.org/10.1891/1541-6577.31.1.56. doi: 10.1891/1541-6577.31.1.56. 28. Codish S, Shiffman RN. A model of ambiguity and vagueness in clinical practice guideline recommendations. AMIA ... Annual Symposium proceedings. 2005;2005:146-150. https://www.ncbi.nlm.nih.gov/pubmed/16779019. 29. Balogh EP, Miller BT, Ball JR. Improving diagnosis in healthcare. Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine; National Academies of Sciences, Engineering, and Medicine. 2015. 30. Singh H, Graber ML. Improving diagnosis in health care. The New England journal of medicine. 2015;373(26):2493-2495
• 15. National Institutes of Health. NIH designates people with disabilities as a population with health disparities. https://www.nih.gov/news-events/news-releases/nih-designates-people- disabilities-population-health-disparities. 16. Seervai S, Shah A, Shah T. The challenges of living with a disability in america, and how serious illness can add to them. 2019. 17. McBride-Henry K, Nazari Orakani S, Good G, Roguski M, Officer TN. Disabled people's experiences accessing healthcare services during the COVID-19 pandemic: A scoping review. BMC health services research. 2023;23(1):346. https://www.ncbi.nlm.nih.gov/pubmed/37024832. doi: https://doi.org/10.1186/s12913-023- 09336-4. 18. Courtney-Long EA, Romano SD, Carroll DD, Fox MH. Socioeconomic factors at the intersection of race and ethnicity influencing health risks for people with disabilities. J Racial and Ethnic Health Disparities. 2017;4(2):213-222. https://www.jstor.org/stable/48707792. doi: 10.1007/s40615-016-0220-5. 19. Magana S, Parish S, Morales MA, Li H, Fujiura G. Racial and ethnic health disparities among people with intellectual and developmental disabilities. Intellect Dev Disabil. 2016;54(3):161-172. doi: 10.1352/1934-9556-54.3.161 [doi]. 20. Havercamp SM, Scott HM. National health surveillance of adults with disabilities, adults with intellectual and developmental disabilities, and adults with no disabilities. Disabil Health J. 2015;8(2):165-172. Version 5/21/25 Page 39 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 http://ezproxy.rush.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh &AN=2015-12464-006&site=ehost-live. doi: 10.1016/j.dhjo.2014.11.002. 21. Van Dyke ME, Mendoza MCB, Li W, et al. Racial and ethnic disparities in COVID-19 incidence by age, sex, and period among persons aged MMWR. Morbidity and mortality weekly report. 2021;70(11):382-388. https://www.ncbi.nlm.nih.gov/pubmed/33735165. doi: 10.15585/mmwr.mm7011e1.
• 8. Sun M, Oliwa T, Peek ME, Tung EL. Negative patient descriptors: Documenting racial bias in the electronic health record. Health Aff. 2022;41(2):203-211. https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155210482&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1377/hlthaff.2021.01423. 9. Siems A, Banks R, Holubkov R, et al. Structured chart review: Assessment of a structured chart review methodology. Hospital pediatrics. 2020;10(1):61-69. https://www.ncbi.nlm.nih.gov/pubmed/31879317. doi: 10.1542/hpeds.2019-0225. Version 5/21/25 Page 37 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 10. Sarkar S, Seshadri D. Conducting record review studies in clinical practice. Journal of clinical and diagnostic research. 2014;8(9):JG01-JG04. https://www.ncbi.nlm.nih.gov/pubmed/25386466. doi: 10.7860/JCDR/2014/8301.4806. 11. Steenbruggen RA, Maas MJM, Hoogeboom TJ, Brand PLP, van der Wees PJ. The application of the tracer method with peer observation and formative feedback for professional development in clinical practice: A scoping review. Perspect Med Educ. 2022;11(1):15-21. https://link.springer.com/article/10.1007/s40037-021-00693-6. doi: 10.1007/s40037-021-00693- 6. 12. Murphy DR, Meyer AND, Bhise V, et al. Computerized triggers of big data to detect delays in follow-up of chest imaging results. Chest. 2016;150(3):613-620. https://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=117707565&authtype=sso& custid=cls59&site=ehost-live. doi: 10.1016/j.chest.2016.05.001. 13. Cogswell ME, Coil E, Tian LH, et al. Health needs and use of services among children with developmental disabilities - united states, 2014-2018. MMWR. Morbidity and mortality weekly report. 2022;71(12):453-458. https://www.ncbi.nlm.nih.gov/pubmed/35324879. doi: 10.15585/mmwr.mm7112a3. 14. Centers for Disease Control and Prevention. Disability impacts all of us. https://www.cdc.gov/ncbddd/disabilityandhealth/infographic-disability-impacts-all.html.
• 1. Agaronnik ND, El-Jawahri A, Iezzoni LI. Perspectives of patients with pre-existing mobility disability on the process of diagnosing their cancer. J GEN INTERN MED. 2021;36(5):1250- 1257. https://link.springer.com/article/10.1007/s11606-020-06327-7. doi: 10.1007/s11606-020- 06327-7. 2. Hallyburton A. Diagnostic overshadowing: An evolutionary concept analysis on the misattribution of physical symptoms to pre-existing psychological illnesses. International journal of mental health nursing. 2022;31(6):1360-1372. https://onlinelibrary.wiley.com/doi/abs/10.1111/inm.13034. doi: 10.1111/inm.13034. 3. Schildkrout B. 5 mental health diagnostic challenges: Update on "to err is human". Psychiatric Times. ;33(Feb 18, 2016). https://www.psychiatrictimes.com/view/5-mental-health-diagnostic- challenges-update--err-human. 4. Demmin DL, Silverstein SM. Visual impairment and mental health: Unmet needs and treatment options. Clinical ophthalmology (Auckland, N.Z.). 2020;14:4229-4251. https://www.ncbi.nlm.nih.gov/pubmed/33299297. doi: 10.2147/OPTH.S258783. Version 5/21/25 Page 36 of 64ORA: 23111505-IRB01 Date IRB Approved: 3/15/2026 Amendment Date: 5/20/2026 5. Druel V, Hayet H, Esman L, Clavel M, Rougé Bugat M. Assessment of cancers' diagnostic stage in a deaf community - survey about 4363 deaf patients recorded in french units. BMC Cancer. 2018;18(1):93. https://www.ncbi.nlm.nih.gov/pubmed/29361910. doi: 10.1186/s12885- 017-3972-3. 6. Escude C. Advancing health equity for people with intellectual and developmental disabilities. Health Affiair Forefront. 2022. https://healthaffairs.org/content/forefront/advancing-health- equity-people-intellectual-and-developmental-disabilities. 7. Hendriksen JGM, Peijnenborgh JCAW, Aldenkamp AP, Vles JSH. Diagnostic overshadowing in a population of children with neurological disabilities: A cross sectional descriptive study on acquired ADHD. European journal of paediatric neurology. 2015;19(5):521-524. doi:10.1016/j.ejpn.2015.04.004.

Study record dates

Study Major Dates

• Study Start (Actual): November 22, 2024
• Primary Completion (Estimated): March 31, 2029
• Study Completion (Estimated): July 31, 2029

Study Registration Dates

• First Submitted: September 18, 2024
• First Submitted That Met QC Criteria: September 20, 2024
• First Posted (Actual): September 23, 2024

Study Record Updates

• Last Update Posted (Actual): August 5, 2026
• Last Update Submitted That Met QC Criteria: August 3, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Algorithms; Disabilities; Diagnostic error; Diagnostic overshadowing
• Additional Relevant MeSH Terms: Health Services Administration; Health Care Quality, Access, and Evaluation; Quality of Health Care; Quality Indicators, Health Care; Socioeconomic Factors; Population Characteristics; Standard of Care; Educational Status
• Other Study ID Numbers: R01HD116320 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Quantitative data generated from baseline and post-analysis of CPT evaluation and management (E/M) code usage and scores on the Safer DX Checklist for patients with one or more of 216 diagnoses prone to error by the presence of specific disabilities versus no presence and demographics. Data will be from patients aged 3-89 years old. Data will be age-disagregated in groupings of five years, except the group aged 3-5 years old.

IPD Sharing Time Frame

End Diagnostic Overshadowing program information will be shared on clinicaltrials.gov upon funding. Information will be updated at least yearly. The information will be submitted according to policy in the NIH "Policy on the Dissemination of NIH-Funded Clinical Trial Information." RUSH University Medical Center has policies in place to ensure that research registration and results reporting occur in compliance the NIH policy.

Data will be findable and identifiable via standard indexing tools available through clinicaltrials.gov.

Data will be made available to other users at least by the time of associated publications or end of the performance period. performance period. Data will be available for a minimum of five years after the last publication. If there is a funded competitive application, data preservation and sharing will continue into the time of the new application and for at least five years after the last associated publication.

• IPD Sharing Access Criteria: Completed baseline data are available in a repository at Rush Universoity. All data deposited were stripped of identifying information. Efforts will be made to maximize sharing of data stripped of identifiers. Will need a request for data with justification to PI at Rush University. This research is approved as exempt by the Rush University IRB. Consent will thus not be a consideration in data sharing. If consent is needed for any portion of this research, data sharing will be made clear in the consent.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No