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 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 it, 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.

A baseline audit of diagnostic processes is being conducted, including data from the Safer DX Checklist and CPT Evaluation and Management (E/M) code usage, for patients aged ≥ 3 to 89 years old with one or more of 26 diagnoses prone to error to compare patients with specific disabilities (major mobility impairments, mental health concerns, severe visual impairments/ blindness, severe hearing loss/deafness, and IDD versus cases of patients without the specific disabilities.

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

2) Manual chart reviews are being conducted on PWDs with specific diagnoses prone to error addressing demographic characteristics: age increments, gender, race/ethnicity, urban/rural, co-morbidities, disability type, insurance type, and severity of illness [relevant to site]). The Safer Dx Checklist (used in multiple previous studies) is being used to evaluate for diagnostic error - specifically diagnostic delay- in encounters. Data on use of CPT E/M codes is being collected including the specific code and date of use. Data are being collected on 10 issues suggested by staff and advocates: caregiver involved, transfer/referral from other institution, verbal, co-morbidity numbers and types, any refused tests, previous history visits for same issue, ongoing use of medical equipment,

The hypothesis is that there will be information that can be used in development of mock tracers and in the Participatory Planning and Decision-Making process to identify themes that underly diagnostic overshadowing.

3) Based on found differences from baseline evaluation of differences in diagnostic processes with patients with disabilities and specific diagnoses prone to error and follow-up chart reviews, targeted interviews with staff will be done. There will be follow up with stakeholders and development of Joint Commission-style individual mock tracers, following the care of specific populations of PWD listed above and system of care tracers focused on evaluating the system surrounding diagnostic overshadowing.

The hypothesis is that there will be information sufficient to develop mock tracers following the care of the specific populations of PWD.

Aim 2: Co-produce a frame of themes underlying diagnostic overshadowing (DO) to develop algorithms to identify patients with the specified disabilities at risk for DO. Case studies for education on DO, and EHR prompts, alerts, and decision supports will be co-produced related to the algorithms, along with education on the EHR materials.

The hypothesis is that there will be information that can be used to develop algorithms for identifying PWD from the specific populations at risk of DO as evidenced by diagnostic process data from the Safer DX Checklist and usage of CPT E/M codes.

Aim 3: Evaluate for change after implementation of algorithms to identify patients with the specified disabilities at risk for DO.

The hypothesis is that there will be statistical change in time to diagnostic evaluation for PWD from the specified 5 groups with the specified diagnoses prone to error.

Data evaluation. 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. It is used to assess five 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.

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.

Quality assurance The Joint Commission developed mock tracers to use in preparing for accreditation visits and for 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 in addition to review of patient charts and forms. For this project, questions will center around diagnostic processes (as evaluated using the Safer DX Checklist and use of CPT E/M codes.

EHR prompts and alerts The stage of the diagnostic process 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 diagnostic support (e.g., website), clinicians can access guidelines more easily.

Algorithms. Algorithms are 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. 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.

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 (Figure 1). 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 investigators will work with involved stakeholders in reducing bias in development of algorithms and other materials.

Participatory Planning and Decision Making (PPDM). PPDM is a model-building process based on principles of participatory action research. It is designed for groups to make judgments about multiple alternatives and weigh the importance that each of these play with respect to the outcome of interest. The process first requires participants to identify or confirm broad domains/themes and subdomains/subthemes contributing to the phenomenon under study. Following the identification of themes and subthemes, importance weights are assigned to each and proportional importance weights are then derived. There are eight basic phases to the PPDM process beginning with pre-surveys regarding initial themes to presenting participants, meetings on the themes, and presenting participants with a final model of themes. Based on broad input from members of different groups and communities, staff at the University of Minnesota Institute on Community Integration have successfully used this process to develop conceptual frames for self-determination and healthcare coordination, and to refine the National Quality Forum's Home and Community-based Services Outcome Measurement Framework. These efforts have resulted in frameworks that possess high content, and concurrent and predictive validity. The use of the PPDM model and processes enables a diverse group of voices to be heard in the development of targeted education programs and EHR decision supports, ensuring that materials fit the needs of the multiple groups. In co-production of algorithms as a means of retrospective review for quality assurance and prospective use to identify patients with disabilities at risk of diagnostic overshadowing/diagnostic error, the PPDM process is expected to be useful in addressing bias in materials.

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 DE along with EHR decision supports and prompts/alerts on specific issues. Statistical changes are expected.

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. Statistical changes are expected.

Pre-post time to diagnostic evaluation is planned for 2-3 issues still yet to be determined to address delayed/missed diagnoses. Time to diagnostic evaluation is expected 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, charts of patients in the specific disability groups and with specific diagnoses prone to error will be identified 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, at least five charts of patients from each of the five specific populations of PWD from which data are being collected (25 at each of the five institutions and associated outpatient practices - 125 in total). Evaluation will be for issues which may provide insight into DO. The Safer DX Checklist will be used in the chart reviews. In discussions with staff, suggestions to explore include the same issues listed above in previous work. Notes will be taken 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 review, other issues are determined, an IRB amendment will be submitted to evaluate additional issues. Protocols and training for the chart reviews are in the process of being developed, with expectations of edits during the first round of chart reviews Following the chart reviews, interviews with staff will be conducted in identified areas to explore perceptions of why there may be differences, expecting at least five in-depth interviews and multiple short interviews at each of the five sites that can be conducted during shift "huddles" and staff meetings.

Third, with these baseline data, the Measurement Action Network and the Advocate Advisory Committee will be consulted on development of separate mock tracers following the care of each specific population of PWD prone to diagnostic overshadowing. 25 retrospective tracers will be conducted 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 (25 each at RUSH University Medical Center, RUSH Oak Park Hospital, RUSH Copley Hospital, Rochester General Hospital, and Erie County Medical Center). For tracers, patients with issues such as high acuity, complexity of care (e.g., multiple tests, surgeries), transfers between units, and history of trauma will be chosen, recognizing that issues affecting care differ by population. Also, system of care tracers focused on understanding system facilitators and barriers to reducing diagnostic overshadowing including trauma-informed care will be conducted.

The development and first baseline implementation of mock tracers and any edits will be completed at Rush by the beginnng of Year two. At other systems, mock tracers will be conducted in year 2. 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 (nationally 70% of inpatients at hospitals are processed through 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.

Participatory Planning and Decision-Making (PPDM) Process Introduction. By the end of Year 2, the investigators will have applied a mixed method analysis of CPT E/M audits and analyses, staff interviews, and chart review quantitative data and qualitative notes. An inductive thematic analysis will then be conducted of chart review, staff interview, and mock tracer notes to identify an initial set of themes of underlying mechanisms of diagnostic overshadowing for use in PPDM processes (See Table 1).

Table 1. Participatory Planning and Decision-Making (PPDM) Process. Phase 1 ● Two weeks prior to meeting, participants complete a pre-meeting questionnaire or interview to solicit their thoughts about the initially identified themes related to diagnostic overshadowing.

● Information solicited via a series of open-ended queries through an online survey or interview to elicit responses regarding themes in question.

Phase 2 ● Stakeholder input analyzed using Constant Comparative Analysis.154,155 ● The degree of consensus will be determined as to the themes identified as 'most important' for targeted education programs and EHR decision supports. Preliminary models will be built to reflect the themes and subthemes developed for each PPDM group.

Phase 3 ● Each PPDM group (5-7 persons) will meet for 2 hours; each group will be facilitated by ICI staff.

• Participants respond to questions regarding their group's framework at both theme and subtheme levels:

  • Nominal group procedures will be used to facilitate discussion.156
  • Provisional frame of themes based on Phases 1 & 2 will be presented.
  • The facilitator will guide group discussion of participants moving toward consensus.
  • Nominal group process procedures will be used to facilitate discussion. Phase 4 ● Using tablets supplied by facilitators and ICI-developed software, stakeholders will provide importance weightings on a 0-100 (or 0-10 depending on group) of equal interval scale for each theme and subtheme.
  • All measurement elements identified.

• The highest rated theme and subtheme within a theme will receive a score of 100 (or 10 depending on group).

• All others are 0-99. (Or 0-9 depending on the group).

Phase 5 ● Following ratings, de-identified importance weights of entire groups displayed. Each participant can view:

• Their own importance weights

• De-identified importance weights of all other stakeholders

• Range of importance weights

• Mean importance weights of their PPDM group for all themes and subthemes. Phase 6

  • PPDM groups will meet a second time for one hour.
  • Similarities and differences in importance weights will be discussed among participants.
  • Participants will be given an opportunity to share with others the reasoning behind their importance weights.
  • Movement toward consensus using nominal group processes will be achieved. Phase 7 ● Using tablets, stakeholders will provide a second set of importance weights for all themes and all subthemes.

Phase 8 ● Proportion weights will be automatically computed through the support system.

● Participants will be presented with final model of themes.

Methods The PPDM process will be as part of the co-production of a frame of important themes related to underlying mechanisms of increased risk of DE. Participants in the PPDM process will be people from impacted groups, including PWD, family members, health professionals, health professional students, quality improvement professionals, people with expertise in child health, and representatives from disability-related organizations, health systems, payor organizations, and community-based services.

An initial set of themes will be formulated related to underlying mechanisms of increased risk of DE. The themes will be inclusive are inclusive of children. Approximately 100 participants across impacted groups are expected. There will be initial survey, a meeting to prioritize themes. The initial prioritization will be sent to participants and there will be a second meeting to further confirm and prioritize themes. At each PPDM meeting, a trained facilitator will use nominal group process procedures to guide the group, ensuring stakeholders stay on task, respond to questions, and move toward consensus. To facilitate the virtual PPDM process, ICI staff developed software that works on laptops, tablet computers, and cellphones and that provides a versatile application enabling participants to easily view all themes and subthemes (and relevant definitions) identified as underlying diagnostic overshadowing and to directly enter priority/importance weights of these themes during PPDM meetings. A second staff person, referred to as a "chauffeur," will operate this software system monitoring participant data entry, offering support when necessary, providing a visual display of the de-identified weightings once the rating process has concluded, and recording the proceedings. The themes will be reviewed by the Cross-Disability Advocate Committee, the Education Action Network, and the Steering Committee prior to implementation.

Data Analysis. As part of the PPDM process, data are continuously "analyzed" by stakeholders at each stage. When the weightings of group members are significantly different, discussion is then facilitated to achieve an enhanced degree of consensus. Data analysis plans for this study will be both qualitative and quantitative in nature. Qualitative analysis based on the recorded statements of stakeholders during the PPDM process will be conducted with NVivo software to discern common themes using constant comparative analysis. Each theme produced by the PPDM groups will be summarized. Similarities and differences between the themes and the weighting of themes developed by different PPDM stakeholder groups will be identified. Specific elements identified for each theme and subtheme will then be analyzed again identifying similarities and differences among groups. Finally, the importance weights that PPDM groups assign to the themes and subthemes will be examined for each stage of the PPDM process (Table 1). Findings will represent the themes underlying DE. The Cross-Disability Advocate Committee and the Consortium Action Networks will review the themes, and each will discuss implications of the themes for their work. The Steering Committee will review the process and make recommendations on any adjustments of plans for the next steps.

Program Development. Following Phase 8 of the PPDM process, with a final model of identified themes, algorithms will be developed to identify patients with specific disabilities at risk diagnostic overshadowing/diagnostic errors. The investigators will simultaneously collaborate with Digital and Information services at Rush, specifically EPIC, to develop the EHR prompts, alerts, and decision supports that reduce ambiguity and provide clear guidelines in the diagnostic process. along with EHR decision supports and prompts/alerts on specific issues. Educational materials on the algorithms and the EHR decision supports and prompts/alerts will be developed along with case studies to educate providers on diagnostic overshadowing/diagnostic errors. In these efforts, principles of instructional design in which learning environments and materials are developed in a way that motivates gaining knowledge and skills will be used. Information services will be involved. Staff at Rush and GIDDN, the three medical centers (RUSH University System for Health, Rochester General Hospital, an Erie County Medical Center), and members of the Steering Committee, Cross-Disability Advocate Advisory Committee, and Action Networks will be integrated into these efforts. Notably, the three institutions have structures to implement instructional design and information technology. The Steering Committee, Cross- Disability Advocate Advisory Committee, and the Education Action Network will conduct ongoing review of program development and implementation.

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)91 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.

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

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

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.

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.

Access to data deposited in clinicaltrials.gov will not be limited. All data deposited will be stripped of identifying information. Efforts will be made to maximize sharing of data stripped of identifiers. 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.