Economic Considerations for Implementing Tobacco Cessation Programs in Cancer Care Settings.

Increasingly, research demonstrates economic benefits of tobacco cessation in cancer care, as seen in a new study by Kypriotakis and colleagues of the MD Anderson cessation program, demonstrating median health care cost savings of $1,095 per patient over 3 months. While the cost-effectiveness of tobacco cessation programs from a hospital perspective is important, implementation decisions in a predominantly fee-for-service system, such as in the United States, too often insufficiently value this outcome. Economic barriers, stakeholder disincentives, and payment models all impact program implementation. Combining economic evaluation with implementation research, including assessment of return-on-investment, may enhance sustainability and inform decision-making in cancer care settings. See related article by Kypriotakis et al., p. 217.

Bladder Cancer Survivors Who Do Not Smoke Have Better Longitudinal Health-Related Quality of Life Measures: An Assessment of the Comparative Effectiveness and Survivorship Health in Bladder Cancer (CEASE-BC) Study.

PURPOSE: Cigarette smoking is the most common risk factor for the development of bladder cancer (BC), yet there is a paucity of data characterizing the relationship between smoking status and longitudinal health-related quality of life (HRQoL) outcomes in patients with BC. We examined the association between smoking status and HRQoL among patients with BC. MATERIALS AND METHODS: Data were sourced from a prospective, longitudinal study open between 2014 and 2017, which examined HRQoL in patients aged ≥ 18 years old diagnosed with BC across North Carolina. The QLQ-C30 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core instrument) was administered at 3, 12, and 24 months after BC diagnosis. Our primary exposure of interest was current smoking status. Linear regression using generalized estimating equations was used to analyze the relationship between smoking status and various domains of the QLQ-C30. RESULTS: A total of 154 patients enrolled in the study. Eighteen percent were classified as smoking at 3 months from diagnosis, and packs per day ranged from < 0.5 to 2. When controlling for time from diagnosis, demographic covariates, cancer stage, and treatment type, mean differences for physical function (7.4), emotional function (5.6), and fatigue measures (-8.2) were significantly better for patients with BC who did not smoke. CONCLUSIONS: Patients with BC who do not smoke have significantly better HRQoL scores in the domains of physical function, emotional function, and fatigue. These results underscore the need to treat smoking as an essential component of BC care.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BACKGROUND: The Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources. METHODS: DEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes. RESULTS: In the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8). CONCLUSION: Most C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

The Impact of the COVID-19 Pandemic on Tobacco Treatment Program Implementation at National Cancer Institute-Designated Cancer Centers.

INTRODUCTION: The COVID-19 pandemic disrupted cancer screening and treatment delivery, but COVID-19's impact on tobacco cessation treatment for cancer patients who smoke has not been widely explored. AIMS AND METHODS: We conducted a sequential cross-sectional analysis of data collected from 34 National Cancer Institute (NCI)-designated cancer centers participating in NCI's Cancer Center Cessation Initiative (C3I), across three reporting periods: one prior to COVID-19 (January-June 2019) and two during the pandemic (January-June 2020, January-June 2021). Using McNemar's Test of Homogeneity, we assessed changes in services offered and implementation activities over time. RESULTS: The proportion of centers offering remote treatment services increased each year for Quitline referrals (56%, 68%, and 91%; p = .000), telephone counseling (59%, 79%, and 94%; p = .002), and referrals to Smokefree TXT (27%, 47%, and 56%; p = .006). Centers offering video-based counseling increased from 2020 to 2021 (18% to 59%; p = .006), Fewer than 10% of centers reported laying off tobacco treatment staff. Compared to early 2020, in 2021 C3I centers reported improvements in their ability to maintain staff and clinician morale, refer to external treatment services, train providers to deliver tobacco treatment, and modify clinical workflows. CONCLUSIONS: The COVID-19 pandemic necessitated a rapid transition to new telehealth program delivery of tobacco treatment for patients with cancer. C3I cancer centers adjusted rapidly to challenges presented by the pandemic, with improvements reported in staff morale and ability to train providers, refer patients to tobacco treatment, and modify clinical workflows. These factors enabled C3I centers to sustain evidence-based tobacco treatment implementation during and beyond the COVID-19 pandemic. IMPLICATIONS: This work describes how NCI-designated cancer centers participating in the Cancer Center Cessation Initiative (C3I) adapted to challenges to sustain evidence-based tobacco use treatment programs during the COVID-19 pandemic. This work offers a model for resilience and rapid transition to remote tobacco treatment services delivery and proposes a policy and research agenda for telehealth services as an approach to sustaining evidence-based tobacco treatment programs.

Integrating Tobacco Treatment Into Oncology Care: Reach and Effectiveness of Evidence-Based Tobacco Treatment Across National Cancer Institute-Designated Cancer Centers.

PURPOSE: Quitting smoking improves patients' clinical outcomes, yet smoking is not commonly addressed as part of cancer care. The Cancer Center Cessation Initiative (C3I) supports National Cancer Institute-designated cancer centers to integrate tobacco treatment programs (TTPs) into routine cancer care. C3I centers vary in size, implementation strategies used, and treatment approaches. We examined associations of these contextual factors with treatment reach and smoking cessation effectiveness. METHODS: This cross-sectional study used survey data from 28 C3I centers that reported tobacco treatment data during the first 6 months of 2021. Primary outcomes of interest were treatment reach (reach)-the proportion of patients identified as currently smoking who received at least one evidence-based tobacco treatment component (eg, counseling and pharmacotherapy)-and smoking cessation effectiveness (effectiveness)-the proportion of patients reporting 7-day point prevalence abstinence at 6-month follow-up. Center-level differences in reach and effectiveness were examined by center characteristics, implementation strategies, and tobacco treatment components. RESULTS: Of the total 692,662 unique patients seen, 44,437 reported current smoking. Across centers, a median of 96% of patients were screened for tobacco use, median smoking prevalence was 7.4%, median reach was 15.4%, and median effectiveness was 18.4%. Center-level characteristics associated with higher reach included higher smoking prevalence, use of center-wide TTP, and lower patient-to-tobacco treatment specialist ratio. Higher effectiveness was observed at centers that served a larger overall population and population of patients who smoke, reported a higher smoking prevalence, and/or offered electronic health record referrals via a closed-loop system. CONCLUSION: Whole-center TTP implementation among inpatients and outpatients, and increasing staff-to-patient ratios may improve TTP reach. Designating personnel with tobacco treatment expertise and resources to increase tobacco treatment dose or intensity may improve smoking cessation effectiveness.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. STUDY DESIGN AND METHODS: University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. RESULTS AND SIGNIFICANCE: The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. CONCLUSIONS: Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Systematic review of smoking relapse rates among cancer survivors who quit at the time of cancer diagnosis.

BACKGROUND: Tobacco cessation, at the time of cancer diagnosis, has been associated with better oncologic outcomes. Cancer diagnosis has been shown to serves as a "teachable moment," inspiring tobacco cessation. However, the sustainability of abstinence from smoking is understudied. Similarly, there is a paucity of data regarding the utility of behavioral/pharmacologic intervention to support continued smoking cessation. METHODS: A systematic literature review was conducted in August 2021 with no date limits. Relevant studies that reported tobacco smoking relapse rates for patients who quit at the time of cancer diagnosis were included. Our literature search identified 1620 articles and 29 met inclusion criteria. The primary endpoint of the study was smoking relapse rate. Secondary outcome was a descriptive assessment of behavioral and pharmacologic interventions to promote continued cessation. Exploratory outcomes included a regression analysis to examine associations between study factors and relapse rates. RESULTS: There were 3021 smokers who quit at the time of cancer diagnosis. Weighted overall relapse rate for the study population was 44 % (range 5-57 %). Interventions to support smoking cessation were employed in 17 of the 29 included studies and protocols were heterogenous, including behavioral, pharmacologic, or mixed intervention strategies. Exploratory analysis demonstrated no association between relapse rates and publication year, gender, or study type. Relapse rates were indirectly associated with age (p = .003), suggesting that younger patients were more likely to relapse. CONCLUSION: The sustainability of smoking cessation after a cancer diagnosis is understudied, and existing literature is difficult to interpret due to heterogeneity. Relapse rates remain significant and, although many studies have included the employment of an intervention to promote continued cessation, few studies have measured the effect of a protocolized intervention to support abstinence.

Evaluating a Tobacco Treatment Program's Transition to Telehealth Using a Social Determinants of Health Lens.

INTRODUCTION: While strong associations exist between social determinants of health (SDOH), socioeconomic status, and smoking, these factors are not routinely assessed in tobacco treatment programs (TTP). This study addresses this gap by evaluating a composite metric of SDOH and a measure of access to care to determine program reach before and after the implementation of telehealth tobacco treatment delivery. AIMS AND METHODS: We examined inpatient data from a large TTP during two comparable time periods from April 1, 2019 to September 30, 2019 (pre-telehealth) and from April 1, 2020 to September 30, 2020 (telehealth). The populations were compared using point-of-care data, including 5-digit zip codes mapped to the CDC's Social Vulnerability Index (SVI) and driving distance (in 60-min increments) to the study hospital. Chi-square tests for homogeneity were performed for SVI and driving distance comparisons. RESULTS: While distance distributions were significantly different between the pre-telehealth and telehealth populations (χ 2 = 13.5 (df = 3, N = 3234), p = .004, no significant differences existed in the proportion of SVI categories between the two populations (χ 2 = 5.8 (df = 3, N = 3234), p = .12). In the telehealth population, patients with the highest SVI vulnerability had the greatest proportions living >1 h from the hospital. CONCLUSIONS: This study offers a novel evaluation of tobacco treatment in relation to an SDOH metric (SVI) and care access (distance to the hospital) for inpatient populations. Patient reach, including to those with high vulnerabilities, remained consistent in a transition to telehealth. These methods can inform future reach and engagement of patients who use tobacco products, including patients with high vulnerability or who reside at greater distances from treatment programs. IMPLICATIONS: This study provides the first analysis of inpatient tobacco use treatment (TUT) transition to telehealth delivery of care during the COVID-19 pandemic using the CDC's SVI metric and patient distance to the hospital. The transition resulted in consistent reach to patients at the highest vulnerability. These findings can inform efforts to evaluate SDOH measures and improve reach, engagement, and research on telehealth delivery of inpatient TUT.

An Analysis of Inpatient Tobacco Use Treatment Transition to Telehealth.

INTRODUCTION: During the COVID-19 pandemic, many tobacco users increased their tobacco use, and calls to quitlines decreased. Among inpatients, the pandemic also necessitated a rapid transition of intensive tobacco use counseling to telehealth counseling. No data exist comparing the outcomes of telehealth inpatient counseling with in-person (pre-telehealth) counseling. AIMS AND METHODS: We examined inpatient data from a large tobacco treatment program (TTP) during two comparable time periods 04/01/2019-09/30/2019 (pre-telehealth) and 04/01/2020-09/30/2020 (telehealth). The pre-telehealth and telehealth populations were compared using Pearson's chi-square test for homogeneity on each populations' patient, visit, and medication acceptance characteristics. Reach to "current tobacco users" was analyzed using TTP flowsheet and electronic health record (EHR) data in relation to aggregate EHR data in the data warehouse. RESULTS: Mean monthly tobacco treatment inpatient counseling and outreach visits increased 38.9% in the telehealth period (M = 376, SD = 36.7) compared with the pre-telehealth period (M = 271, SD = 50.0) (t(10) = 3.8, p = .004). Reach significantly increased from 32.8% to 65.9% among all "current tobacco users" admitted, including 31.8% to 66.6% in races at higher risk for COVID-19 severe disease. Pearson's chi-square tests for homogeneity showed significant differences in the pre-telehealth and telehealth population distributions for age, visit type, ethnicity, and medication acceptance. CONCLUSIONS: This study offers the first understanding of characteristics of patients, visits, and medication acceptances in pre-telehealth and telehealth tobacco use treatment for inpatient populations. Larger reach and counseling were identified in the telehealth population. This study's findings on inpatient tobacco use treatment can inform future reach and engagement of large numbers of patients who use tobacco products. IMPLICATIONS: This study provides the first analysis of inpatient tobacco use treatment transition to telehealth delivery of care during the COVID-19 pandemic. The transition resulted in increases in reach and cessation counseling. These findings can inform efforts to improve reach, engagement, and research on telehealth delivery of inpatient tobacco use treatment.

The STUN (STop UNhealthy) Alcohol Use Now trial: study protocol for an adaptive randomized trial on dissemination and implementation of screening and management of unhealthy alcohol use in primary care.

BACKGROUND: Unhealthy alcohol use is a leading cause of preventable deaths in the USA and is associated with many societal and health problems. Less than a third of people who visit primary care providers in the USA are asked about or ever discuss alcohol use with a health professional. METHODS/DESIGN: This study is an adaptive, randomized, controlled trial to evaluate the effect of primary care practice facilitation and telehealth services on evidence-based screening, counseling, and pharmacotherapy for unhealthy alcohol use in small-to-medium-sized primary care practices. Study participants will include primary care practices in North Carolina with 10 or fewer providers. All enrolled practices will receive a practice facilitation intervention that includes quality improvement (QI) coaching, electronic health record (EHR) support, training, and expert consultation. After 6 months, practices in the lower 50th percentile (based on performance) will be randomized to continued practice facilitation or provision of telehealth services plus ongoing facilitation for the next 6 months. Practices in the upper 50th percentile after the initial 6 months of intervention will continue to receive practice facilitation alone. The main outcome measures include the number (and %) of patients in the target population who are screened for unhealthy alcohol use, screen positive, and receive brief counseling. Additional measures include the number (and %) of patients who receive pharmacotherapy for AUD or are referred for AUD services. Sample size calculations determined that 35 practices are needed to detect a 10% increase in the main outcome (percent screened for unhealthy alcohol use) over 6 months. DISCUSSION: A successful intervention would significantly reduce morbidity among adults from unhealthy alcohol use by increasing counseling and other treatment opportunities. The study will produce important evidence about the effect of practice facilitation on uptake of evidence-based screening, counseling, and pharmacotherapy for unhealthy alcohol use when delivered on a large scale to small and medium-sized practices. It will also generate scientific knowledge about whether embedded telehealth services can improve the use of evidence-based screening and interventions for practices with slower uptake. The results of this rigorously conducted evaluation are expected to have a positive impact by accelerating the dissemination and implementation of evidence related to unhealthy alcohol use into primary care practices. TRIAL REGISTRATION: ClinicalTrials.gov NCT04317989 . Registered on March 23, 2020.

Harnessing the electronic health record to advance integrated care.

INTRODUCTION: Integrated health care is utilized in primary care clinics to meet patients' physical, behavioral, and social needs. Current methods to collect and evaluate the effectiveness of integrated care require refinement. Using informatics and electronic health records (EHR) to distill large amounts of clinical data may help researchers measure the impact of integrated care more efficiently. This exploratory pilot study aimed to (a) determine the feasibility of using EHR documentation to identify behavioral health and social care components of integrated care, using social work as a use case, and (b) develop a lexicon to inform future research using natural language processing. METHOD: Study steps included development of a preliminary lexicon of behavioral health and social care interventions to address basic needs, creation of an abstraction guide, identification of appropriate EHR notes, manual chart abstraction, revision of the lexicon, and synthesis of findings. RESULTS: Notes (N = 647) were analyzed from a random sample of 60 patients. Notes documented behavioral health and social care components of care but were difficult to identify due to inconsistencies in note location and titling. Although the interventions were not described in detail, the outcomes of screening, referral, and brief treatment were included. The integrated care team frequently used EHR to share information and communicate. DISCUSSION: Opportunities and challenges to using EHR data were identified and need to be addressed to better understand the behavioral health and social care interventions in integrated care. To best leverage EHR data, future research must determine how to document and extract pertinent information about integrated team-based interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods.

Just under half of the 85.7 million US adults with hypertension have uncontrolled blood pressure using a hypertension threshold of systolic pressure ≥ 140 or diastolic pressure ≥ 90. Uncontrolled hypertension increases risks of death, stroke, heart failure, and myocardial infarction. Guidelines on hypertension management include lifestyle modification such as diet and exercise. In order to improve hypertension control, it is important to identify predictors of lifestyle modification assessment or advice to tailor future interventions using these effective, low-risk interventions. Electronic health record data from 14,360 adult hypertension patients at an academic medical center were analyzed using statistical and machine learning methods to identify predictors and timing of lifestyle modification. Multiple variables were statistically significant in analysis of lifestyle modification documentation at multiple time points. Random Forest was the best machine learning method to classify lifestyle modification documentation at any time with Area Under the Receiver Operator Curve (AUROC) 0.831. Logistic regression was the best machine learning method for classifying lifestyle modification documentation at ≤3 months with an AUROC of 0.685. Analyzing narrative and coded data from electronic health records can improve understanding of timing of lifestyle modification and patient, clinic and provider characteristics that are correlated with or predictive of documentation of lifestyle modification for hypertension. This information can inform improvement efforts in hypertension care processes, treatment implementation, and ultimately hypertension control.

Natural language processing of lifestyle modification documentation.

Lifestyle modification, including diet, exercise, and tobacco cessation, is the first-line treatment of many disorders including hypertension, obesity, and diabetes. Lifestyle modification data are not easily retrieved or used in research due to their textual nature. This study addresses this knowledge gap using natural language processing to automatically identify lifestyle modification documentation from electronic health records. Electronic health record notes from hypertension patients were analyzed using an open-source natural language processing tool to retrieve assessment and advice regarding lifestyle modification. These data were classified as lifestyle modification assessment or advice and mapped to a coded standard ontology. Combined lifestyle modification (advice and assessment) recall was 99.27 percent, precision 94.44 percent, and correct classification 88.15 percent. Through extraction and transformation of narrative lifestyle modification data to coded data, this critical information can be used in research, metric development, and quality improvement efforts regarding care delivery for multiple medical conditions that benefit from lifestyle modification.

Patient free text reporting of symptomatic adverse events in cancer clinical research using the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE).

OBJECTIVE: The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies. MATERIALS AND METHODS: Patients in 3 multicenter trials electronically completed surveys during cancer treatment. Each survey included a prespecified subset of items from the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Upon completion of the survey items, patients could add supplemental symptomatic AE information in a free text box. As patients typed into the box, structured dropdown terms could be selected from the PRO-CTCAE item library or Medical Dictionary for Regulatory Activities (MedDRA), or patients could type unstructured free text for submission. RESULTS: Data were pooled from 1760 participants (48% women; 78% White) who completed 8892 surveys, of which 2387 (26.8%) included supplemental symptomatic AE information. Overall, 1024 (58%) patients entered supplemental information at least once, with an average of 2.3 per patient per study. This encompassed 1474 of 8892 (16.6%) dropdowns and 913 of 8892 (10.3%) unstructured free text entries. One-third of the unstructured free text entries (32%) could be mapped post hoc to a PRO-CTCAE term and 68% to a MedDRA term. DISCUSSION: Participants frequently added supplemental information beyond study-specific survey items. Almost half selected a structured dropdown term, although many opted to submit unstructured free text entries. Most free text entries could be mapped post hoc to PRO-CTCAE or MedDRA terms, suggesting opportunities to enhance the system to perform real-time mapping for AE reporting. CONCLUSIONS: Patient reporting of symptomatic AEs using a text box functionality with mapping to existing terminologies is both feasible and informative.

IRB Process Improvements: A Machine Learning Analysis.

OBJECTIVE: Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. METHODS: We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. RESULTS: Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. CONCLUSIONS: We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations.

OBJECTIVE: The completion of sequencing the human genome in 2003 has spurred the production and collection of genetic data at ever increasing rates. Genetic data obtained for clinical purposes, as is true for all results of clinical tests, are expected to be included in patients' medical records. With this explosion of information, questions of what, when, where and how to incorporate genetic data into electronic health records (EHRs) have reached a critical point. In order to answer these questions fully, this paper addresses the ethical, logistical and technological issues involved in incorporating these data into EHRs. MATERIALS AND METHODS: This paper reviews journal articles, government documents and websites relevant to the ethics, genetics and informatics domains as they pertain to EHRs. RESULTS AND DISCUSSION: The authors explore concerns and tasks facing health information technology (HIT) developers at the intersection of ethics, genetics, and technology as applied to EHR development. CONCLUSIONS: By ensuring the efficient and effective incorporation of genetic data into EHRs, HIT developers will play a key role in facilitating the delivery of personalized medicine.