Routine Clinical Operations Research Articles

B-138 A Harmonization Program Leading to Improved Accuracy and Precision of Hemoglobin A1c (HbA1c) Testing: A Six-Year Multi-Site, Multi-Instrument Review and Comparison

Abstract Background Across four global laboratory locations in North America, Europe, and Asia a quality control system was implemented in the measurement of Hemoglobin A1c (HbA1c). The intent was to improve accuracy and reduce imprecision, both being independent contributors to total analytical performance. Methods Quality control (QC) data, created during routine clinical operations, was used for this analysis. Analysis of QC data evaluated bias and precision separately and provided the ability to predict and monitor performance. The in-process harmonization program indicated a change in instrumentation was needed to meet and maintain a more stringent analytical error profile. The instrumentation change allowed for a before and after comparison across platforms. Results Use of a harmonized quality control system allowed for the visualization of the role bias and precision play in measurements. Improved performance was confirmed using native matrix samples tested routinely from participation in the National Glycohemoglobin Standardization Program. Independently assessing bias and precision highlighted the cumulative and offsetting effects that challenge quality assessment and risk mitigation. Bias appeared to have both a transient and systematic nature. The transient bias contributed to the overall normal distributive property of the quality data. In contrast, the systematic accumulation of bias appeared to occur slowly, over multiple reagent and calibration cycles. The accumulation of bias occurred in the direction of the assay drift. While precision commonly consumes the largest amount of analytical error within an instrument, the dual (negative and positive) nature of bias similarly affects the harmonization of an instrument group. Conclusions Changing the way data is viewed to include assessments of bias improved performance and aided in the identification of opportunities to reduce analytical variances that results in inter- and intra-individual variation. Harmonization of instruments should include methods to assess and describe bias, independent of precision.

Development of a standalone delivery sequence model for proton arc therapy.

Spot-scanning proton arc (SPArc) has been drawing significant interests in recent years because of its capability of continuous proton irradiation during the gantry rotation. Previous studies demonstrated SPArc plans were delivered on a prototype of the DynamicARC solution, IBA ProteusONE. We built a novel delivery sequence model through an independent experimental approach: the first SPArc delivery sequence model (DSMSPArc). Based on the model, we investigated SPArc treatment efficiency improvement in the routine proton clinical operation. SPArc test plans were generated and delivered on a prototype of the DynamicARC solution, IBA ProteusONE. An independent gantry inclinometer and the machine logfiles were used to derive the DSMSPArc. Seventeen SPArc plans were used to validate the model's accuracy independently. Two random clinical operation dates (6th January and 22nd March, 2021) from a single-room proton therapy center (PTC) were selected to quantitatively assess the improvement of treatment efficiency compared to the IMPT. The difference between the logfile and DSMSPArc is about 3.2±4.8%. SPArc reduced 58.1% of the average treatment delivery time per patient compared to IMPT (p<0.01). Daily treatment throughput could be increased by 30% using SPArc using a single-room proton therapy system. The first model of dynamic arc therapy is established in this study through an independent experimental approach using logfiles and measurements which allows clinical users and investigators to simulate the dynamic treatment delivery and assess the daily treatment throughput improvement.

Changes in Patient Perceptions of the Provider Most Involved in Care During COVID-19 and Corresponding Effects on Patient Trust.

During COVID-19 routine clinical operations were disrupted, including limits on the types of providers allowed to perform in-person care and frequency of times they could enter a patient's room. Whether these changes affected patients' trust in the care they received during hospitalization is unknown. Hospitalized patients on the general medicine service were called after discharge and asked to identify who (attending, resident, etc.) was most involved in their inpatient care, and how much trust they had in the physician caring for them. During the pandemic patients were more likely to report attending physicians (29% to 34%) and nurses (30% to 35%), and less likely to report residents/interns (8.1% to 6.5%) or medical students (1.7% to 1.4%) as most involved in their care (chi-squared test, p = 0.04). Patients reporting their attending physician as most involved in their care were more likely to report trusting their doctor (chi-squared test, p < 0.01). As such, trends in medical education that limit trainees' time in direct patient care may affect the development of clinical and interpersonal skills necessary to establish patient trust.

Abstract 10253: Integrating Mental Health Screening Into a Health System for Measurement-Based Care of Patients With Peripheral Artery Disease

Introduction: Multi-dimensional assessments of patient reported outcomes (PROs) are becoming increasingly important in optimizing patient-centered care for chronic conditions such as peripheral artery disease (PAD). We aimed to demonstrate the feasibility of a health system integrated workflow to incorporate mental health screenings into the outpatient PAD clinic workflow. Methods: In a large academic center, we administered three validated psychological assessments to consecutive outpatients between March 3, 2021 and July 7, 2021. Assessments were administered as part of an in-person patient navigation system (Yale PRO-QI) and was supported by online data collection. Assessments included the Patient Health Questionnaire-8 (PHQ-8) for depression, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety, and the Perceived Stress Scale-4 (PSS-4) for stress. Validated cutoffs were used to screen for depression and anxiety ( &gt; 10 = depression or anxiety, respectively) and stress (&gt;6 = high stress). Results: There were 104 assessments collected for 98 participants with a mean age of 68.6 + 12.0 years old, 38.8% female, 86.7% White, 9.1% Black and 2.0% Asian or American Indian. The majority (72.5%) were seen for a routine follow-up visit. Two participants (2.0%) screened positive for generalized anxiety disorder. For depression, 12.2% screened positive. Almost half of all participants (47.9%) had higher levels of stress compared with a normative population. There were no statistically significant associations between the risk of positive screening and visit type. Conclusions: Measurement-based care metrics are becoming a standard best practice in routine clinical operations. Psychosocial screening instruments can be integrated in PAD outpatient care and highlight opportunities to improve integrated behavioral care pathways to provide the best care for this vulnerable population.

Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks

IntroductionCone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. MethodsCBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. ResultsThe success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. ConclusionsAlthough PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.

Artificial Intelligence in Dentistry: Past, Present, and Future.

Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

Suicide-Specific Training Experiences and Needs Among Military Mental Health Providers

Background Military suicide rates have risen over the past two decades, with a notable spike in recent years. To address this issue, military mental health providers must be equipped with the skills required to provide timely and effective care; yet little is known about the suicide-specific training experiences or needs of these professionals. Methods Thirty-five mental health care providers who treat active duty personnel at military treatment facilities participated in this mixed-methods study. All participants completed a survey assessing training and clinical experiences, comfort and proficiency in working with patients at risk for suicide, and perceived barriers to obtaining suicide-specific training. A sub-set of participants (n = 8) completed a telephone interview to further describe previous experiences and perceived challenges to obtaining training. Results The majority of participants (79.4%) had 6+ years of clinical experience, had a patient who had attempted suicide (85.3%), and completed at least one suicide-related training since finishing their education (82.4%). Survey results showed the leading barrier to enrolling in suicide-specific trainings was perceived lack of training opportunities (40.7% reported it was a barrier “quite often” or more), followed by lack of time (25%). Interview results revealed lack of time, location and logistical issues, and low perceived need for additional training among providers could impede enrollment. Conclusions Study results identified several modifiable barriers to receiving suicide-specific continuing education among military mental health providers. Future efforts should develop accessible training programs that can be easily integrated into routine clinical operations to mount the best defense against suicide. HIGHLIGHTS Military mental health providers report significant experience and relatively high degrees of comfort and proficiency working with patients at high risk for suicide. Most providers reported receiving training in suicide assessment and screening; few reported prior training in management of suicidality. Study results identified several modifiable barriers to receiving suicide-prevention continuing education among military mental health care providers; future efforts should seek to develop accessible training programs that can be easily integrated into routine clinical operations to mount the best defense against suicide.

Integration of Patient-reported Outcomes Assessment Into Routine Care for Patients Receiving Residential Treatment for Alcohol and/or Substance Use Disorder.

More than 3 million individuals receive treatment for alcohol use disorder (AUD) and/or substance use disorder each year, yet there exists no standardized method for measuring patient success in treatment. Quantifying a more comprehensive assessment of treatment outcomes could identify the relative efficacy of different treatment strategies for individuals with AUD/substance use disorders, and help patients to identify, in advance, appropriate treatment options. This study developed and embedded patient-reported outcome measures into the routine clinical operations of a residential treatment program. Surveys assessed demographics, drug use history, physical and mental health, and quality of life. Outcomes were assessed among participants at admission (n = 961) and in patients who completed the survey at time of discharge (n = 633). Past 30-day alcohol and/or opioid use at admission were correlated with worse self-reported physical and mental health, sleep, and quality of life, and greater negative affect and craving ( P s < 0.05). Previous history of treatment and/or withdrawal management were associated with worse self-reported physical and mental health, quality of life, and increased craving ( P s < 0.05). Physical and mental health improved across timepoints and was most pronounced when comparing persons receiving treatment for opioid use disorder versus AUD, wherein persons with opioid use disorder had worse physical health at all time points, and greater sleep disturbance and negative affect at discharge ( P s < 0.05). It is feasible to embed patient outcome monitoring into routine clinic operations, which could be used in the future to tailor treatment plans.

Technology-assisted methadone take-home dosing for dispensing methadone to persons with opioid use disorder during the Covid-19 pandemic

IntroductionCovid-19 confers substantial risk for the >400,000 patients who receive methadone for the treatment of opioid use disorder (OUD) and methods for safely dispensing large quantities of methadone to patients are lacking. MethodsThis study evaluated the MedMinder “Jon”, an electronic and cellular-enabled pillbox that provides real-time monitoring to remotely manage take-home doses of methadone using a 12-week, within-subject, Phase II (NCT03254043) trial. We transitioned all participants from liquid to tablet methadone one week prior to randomization. Participants completed both treatment-as-usual and electronic pillbox conditions before choosing a condition in a final “choice phase”. We assessed feasibility, satisfaction, and safety outcomes during the exit interview. ResultsOverall, we randomized 25 participants, 24 (96.0%) completed >1 study session, and 21 (84.0%) completed the exit interview. We dispensed 167.92 g (1,974 doses) of methadone. Participants would use the pillbox again (86.3%) and recommend it to others (95.4%). Overall, 52.4% selected the pillbox in the choice condition and those who did not cited issues related to study requirements. Less than 1% of pillbox alerts were for medication being consumed outside the dosing window and we observed no evidence of actual or attempted methadone diversion. DiscussionWe were able to adequately manage patients who would not otherwise qualify for large quantities of take-home methadone when we dispensed methadone tablets via a secure pillbox. The integration of a commercially available pillbox into routine clinic operations increases opportunity for dispensing medication. Our data support remote monitoring of methadone take-home doses and may inform clinic practices related to Covid-19.

Using in vivo EPID images to detect and quantify patient anatomy changes with gradient dose segmented analysis

To investigate the utility of gradient dose segmented analysis (GDSA) in combination with in vivo electronic portal imaging device (EPID) images to predict changes in the PTV mean dose for patient cases. Also, we use the GDSA to retrospectively analyze patients treated in our clinic to assess deviations for different treatment sites and use time-series data to observe any day-to-day changes. In vivo EPID transit images acquired on the Varian Halcyon were analyzed for simulated errors in a phantom, including gas bubbles, weight loss, patient shifts, and an arm erroneously in the field. GDSA threshold parameters were tuned to maximize the coefficient of determination (R2 ) between GDSA metrics and the change in the PTV mean dose (Dmean ) as estimated in a treatment planning system (TPS). Similarly for a gamma analysis, the gamma criteria were adjusted to maximize R2 between gamma pass rate and the change in the PTV Dmean from the TPS. The predictive accuracy of these models was tested on patient data measuring the mean and standard deviation of the difference in the predicted change in PTV Dmean and the change in PTV Dmean measured in the TPS. This analysis was extended retrospectively for every patient treated over a 23-month period (n=852 patients) to assess the range of expected deviations that occurred during routine clinical operation, as well as to assess any differences between treatment sites. Grouping patients treated on the same day, a time-series analysis was performed to determine if GDSA metrics could add value in tracking machine behavior over time. For the phantom data, analyzing the errors, except for shifts, and comparing the change in PTV Dmean and GDSA mean, a maximal R2 =0.90 was found for a dose threshold of 5% and gradient threshold of 3mm. For the gamma approach a linear fit between the gamma pass rate for change in the PTV Dmean was assessed for different criteria, using the same image data. A maximal, R2 =0.84 was found for a gamma criteria of 3%/3mm, 45% lower dose threshold. For patient data, the predictive accuracy of the change in the PTV Dmean using the GDSA approach and the gamma approach was 0.09±0.98 % and-0.65±2.21%, respectively. Comparing the two approaches the accuracy did not significantly differ (P=0.38), whereas the precision of the GDSA prediction is significantly less (P<0.001). The dosimetric impact of shifts was not detectable with either the GDSA or gamma approach. Analysis of all patients treated over 23months showed that over 95% of fractions treated deviated from the first fraction by 2% or less. Deviations>2% occurred most frequently for the later fractions of head-and-neck and lung treatments. Additionally, averaging the GDSA mean metric over all patients on a given treatment day showed that changes in the machine output on the order of 1% could be identified. GDSA of in vivo EPID images is a useful technique for monitoring patient changes during the course of treatment, particularly weight loss and tumor shrinkage. The GDSA mean provides a quantitative estimate of the change in the PTV Dmean , giving a simple, quantitative metric by which to flag patients with clinically meaningful deviations in treatment. Averaging the GDSA metric over all patients treated on a given day and tracking daily variations can also provide a flag for any systematic deviations in treatment due to machine performance.

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

Community-Based Drive-Through and Walk-Through Testing Centers

Patients with active coronavirus disease 2019 (COVID-19) must be isolated and quarantined, making rapid identification of disease in large numbers of patients a critical component of the pandemic response. For this to happen, a laboratory must be available with sufficient diagnostic testing capacity, and large volumes of specimens collected and tests run, and with results communicated efficiently. In addition, freestanding test-collection sites must be established away from routine clinical operations to provide dedicated space where large numbers of specimens can be collected.

The High Value of Blurry Data in Improving Pediatric Emergency Care.

Pediatric emergency care research suffers from a generalizability problem. Such research in the United States largely relies on data collected from within academic medical centers or large health systems with the infrastructure to conduct and publish clinical research. Such research also includes studies ranging from trials of fluid infusion rates for diabetic ketoacidosis1 from the Pediatric Emergency Care Applied Research Network to retrospective cohort studies defining the incidence of herpes simplex virus by week of life.2 This research is extremely valuable because it informs how to distribute limited health care resources and where to focus efforts to improve care. However, academic and large health systems are a narrow lens through which to view care because 90% of children do not visit academic pediatric emergency departments (EDs) but rather visit community general EDs.3 Children who visit academic EDs differ from the larger population of children demographically, especially related to their access to, need for, and receipt of acute care. Because of these differences, epidemiological insights cannot always be generalized beyond the populations that academic centers serve. Large, population-based, administrative databases offer 1 solution to this problem. Administrative data are collected in the course of routine clinical operations and include claims, which are billing records. These databases do not typically contain clinical information and instead contain demographics, diagnoses, procedures, and billing information, such as charges. They therefore represent a blurrier but wider field of view than the narrow lens of academia-based investigation, thereby allowing more complete insight into diseases and outcomes. Furthermore, these databases eliminate the selection bias introduced by studying only children who present to academic hospitals, which often outweighs the advantages of prospective data collection. The main advantages of administrative data …

Lessons Learned From Hurricane Maria in Puerto Rico: Practical Measures to Mitigate the Impact of a Catastrophic Natural Disaster on Radiation Oncology Patients

PurposeAlthough the wind, rain, and flooding of Hurricane Maria in Puerto Rico abated shortly after its landfall on September 20, 2017, the disruption of the electrical, communications, transportation, and medical infrastructure of the island was unprecedented in scope and caused lasting harm for many months afterward. A compilation of recommendations from radiation oncologists who were in Puerto Rico during the disaster, and from a panel of American Society for Radiation Oncology (ASTRO) cancer experts was created. Methods and materialsRadiation oncologists throughout Puerto Rico collaborated and improvised to continue treating patients in the immediate aftermath of the storm and as routine clinical operations were restored gradually. Empirical lessons from the experience of radiation therapy administration in this profoundly altered context of limited resources, impaired communication, and inadequate transportation were organized into a recommended template, applicable to any radiation oncology practice. ASTRO disease-site experts provided evidence-guidelines for mitigating the impact of a 2- to 3-week interruption in radiation therapy. ResultsPractical measures to mitigate the medical impact of a disaster are summarized within the framework of “Prepare, Communicate, Operate, Compensate.” Specific measures include the development of an emergency operations plan tailored to specific circumstances, prospective coordination with other radiation oncology clinics before a disaster, ongoing communications with emergency management organizations, and routine practice of alternate methods to disseminate information among providers and patients. ConclusionsThese recommendations serve as a starting point to assist any radiation oncology practice in becoming more resiliently prepared for a local or regional disruption from any cause. Disease-site experts provide evidence-based guidelines on how to mitigate the impact of a 2- to 3-week interruption in radiation therapy for lung, head and neck, uterine cervix, breast, and prostate cancers through altered fractionation or dose escalation.

Improving Breast MR Wait Times: A Model for Transitioning Newly Implemented Diagnostic Imaging Procedures into Routine Clinical Operation

PurposeThe purpose of this quality improvement (QI) initiative was to increase patient access to breast MR while maintaining diagnostic image quality. MethodsInstitutional review board approval was waived for this HIPAA-compliant QI initiative, which was conducted from December 2014 through March 2016. Breast MR wait times, scheduling grids, and staffing models were reviewed to identify root causes of elevated wait times. Breast MR wait times were tracked on a biweekly basis as root causes were identified and action plans were implemented. Patient recall rates for repeat MR imaging were tracked. A retrospective analysis of image quality was performed in a randomly selected sample (20 per month; total: 320 examinations). Wait time and image quality data were analyzed with statistical process control charts and logistic regression. ResultsIn all, 798 breast MR examinations were performed during the study period. Monthly volume increased from 23 in December 2014 to 50 in March 2016 (range: 23-64). Wait time for a routine breast MRI fell from 101 days before implementation to 5 days at study completion. The technical recall rate was 0.5% (4 of 798); no recall was performed for a technologist-related error or scan quality concern. The proportion of examinations with minor (31% [99 of 320]) or major (3% [9 of 320]) image quality impairments did not significantly change during the study period (P = .69-.70). ConclusionA specialized MR examination was transitioned into routine clinical operation while maintaining image quality. This model may be useful for transitioning other specialized diagnostic imaging examinations into routine clinical practice.

INFLUENCE OF HEALTH TECHNOLOGY ASSESSMENT AND ITS MEASUREMENT.

The aim of this study was to obtain information on methods used to measure health technology assessment (HTA) influence, decisions that were influenced, and outcomes linked to HTA. Electronic databases were used to locate studies in which HTA influence had been demonstrated. Inclusion criteria were studies that reliably reported consideration by decision makers of HTA findings; comparative studies of technology use before and after HTA; and details of changes in policy, health outcomes, or research that could be credibly linked to an HTA. Fifty-one studies were selected for review. Settings were national (24), regional (12), both national and regional (3) hospitals (9), and multinational (3). The most common approach to appraisal of influence was review of policy or administrative decisions following HTA recommendations (51 percent). Eighteen studies (35 percent) reported interview or survey findings, thirteen (26 percent) reviewed administrative data, and six considered the influence of primary studies. Of 142 decisions informed by HTA, the most common types were on routine clinical practice (67 percent of studies), coverage (63 percent), and program operation (37 percent). The most frequent indications of HTA influence were on decisions related to resource allocation (59 percent), change in practice pattern (31 percent), and incorporation of HTA details in reference material (18 percent). Few publications assessed the contribution of HTA to changing patient outcomes. The literature on HTA influence remains limited, with little on longer term effects on practice and outcomes. The reviewed publications indicated how HTA is being used in different settings and approaches to measuring its influence that might be more widely applied, such as surveys and monitoring administrative data.

Big Data and the Future of Radiology Informatics

Rapid growth in the amount of data that is electronically recorded as part of routine clinical operations has generated great interest in the use of Big Data methodologies to address clinical and research questions. These methods can efficiently analyze and deliver insights from high-volume, high-variety, and high-growth rate datasets generated across the continuum of care, thereby forgoing the time, cost, and effort of more focused and controlled hypothesis-driven research. By virtue of an existing robust information technology infrastructure and years of archived digital data, radiology departments are particularly well positioned to take advantage of emerging Big Data techniques. In this review, we describe four areas in which Big Data is poised to have an immediate impact on radiology practice, research, and operations. In addition, we provide an overview of the Big Data adoption cycle and describe how academic radiology departments can promote Big Data development.

A Pilot Randomized Trial of Financial Incentives or Coaching to Lower Serum Phosphorus in Dialysis Patients

Among chronic hemodialysis patients, hyperphosphatemia is common and associated with mortality. Behavioral economics and complementary behavior-change theories may offer valuable approaches to achieving phosphorus (PO4) control. The aim was to determine feasibility of implementing financial incentives and structured coaching to improve PO4 in the hemodialysis setting. This pilot randomized controlled trial was conducted in 3 urban dialysis units for 10weeks among 36 adults with elevated serum PO4 (median >5.5mg/dL over 3months). Twelve participants each were randomized to: (1) financial incentives for lowering PO4, (2) coaching about dietary and medication adherence, or (3) usual care. PO4 was measured during routine clinic operations. Each incentives arm participant received the equivalent of $1.50/day if the PO4 was ≤5.5mg/dL or >5.5mg/dL but decreased ≥0.5mg/dL since the prior measurement. The coach was instructed to contact coaching arm participants at least 3 times per week. The outcome measures included: (1) enrollment rate, (2) dropout rate, and (3) change in PO4 from beginning to end of 10-week intervention period. Of 66 eligible patients, 36 (55%) enrolled. Median age was 53years, 83% were black race, and 78% were male. Median baseline PO4 was 6.0 (interquartile range 5.6, 7.5). Using stratified generalized estimation equation analyses, the monthly decline in PO4 was -0.32mg/dL (95% CI -0.60, -0.04) in the incentives arm, -0.40mg/dL (-0.60, -0.20) in the coaching arm, and -0.24mg/dL (-0.60, 0.08) in the usual care arm. No patients dropped out. All intervention arm participants expressed interest in receiving similar support in the future. This pilot trial demonstrated good feasibility in enrollment and implementation of novel behavioral health strategies to reduce PO4 in dialysis patients.

On transcending the impasse of respiratory motion correction applications in routine clinical imaging - a consideration of a fully automated data driven motion control framework.

Positron emission tomography (PET) is increasingly used for the detection, characterization, and follow-up of tumors located in the thorax. However, patient respiratory motion presents a unique limitation that hinders the application of high-resolution PET technology for this type of imaging. Efforts to transcend this limitation have been underway for more than a decade, yet PET remains for practical considerations a modality vulnerable to motion-induced image degradation. Respiratory motion control is not employed in routine clinical operations. In this article, we take an opportunity to highlight some of the recent advancements in data-driven motion control strategies and how they may form an underpinning for what we are presenting as a fully automated data-driven motion control framework. This framework represents an alternative direction for future endeavors in motion control and can conceptually connect individual focused studies with a strategy for addressing big picture challenges and goals.Electronic supplementary materialThe online version of this article (doi:10.1186/2197-7364-1-8) contains supplementary material, which is available to authorized users.

Evaluation as Part of Operations: Reconciling the Common Rule and Continuous Improvement

AbstractUnderstanding the components of clinical care that work best is a cornerstone of improving health care. And yet, the more we improve the quality of quality improvement and move to continuous learning about clinical care more broadly, the more we find ourselves in a regulatory environment that makes evaluation more difficult, expensive, and, in some situations, impossible. In their paper on the ethical underpinnings of the distinction between research and treatment, Ruth Faden and colleagues raise important implications for a wide array of situations. These points give reason to rethink the definition of routine clinical operations to include evaluation of the processes and outcomes of care and dissemination of findings. We concur with the assertion by Faden and colleagues, and with previous work from The Hastings Center, that conducting continuous improvement activities such as these is an obligation of health systems and clinicians. We believe that rigorous, systematic evaluation should be considered part of normal, expected operations, rather than exceptional behavior that requires extraordinary regulatory control.