Clinical Workflow Processes Research Articles

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours

Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

Development and utility of a clinical research informatics application for participant recruitment and workflow management for a return of results pilot trial in familial hypercholesterolemia in the Million Veteran Program.

The development of clinical research informatics tools and workflow processes associated with re-engaging biobank participants has become necessary as genomic repositories increasingly consider the return of actionable research results. Here we describe the development and utility of an informatics application for participant recruitment and enrollment management for the Veterans Affairs Million Veteran Program Return Of Actionable Results Study, a randomized controlled pilot trial returning individual genetic results associated with familial hypercholesterolemia. The application is developed in Python-Flask and was placed into production in November 2021. The application includes modules for chart review, medication reconciliation, participant contact and biospecimen logging, survey recording, randomization, and documentation of genetic counseling and result disclosure. Three primary users, a genetic counselor and two research coordinators, and 326 Veteran participants have been integrated into the system as of February 23, 2023. The application has successfully handled 3367 task requests involving greater than 95000 structured data points. Specifically, application users have recorded 326 chart reviews, 867 recruitment telephone calls, 158 telephone-based surveys, and 61 return of results genetic counseling sessions, among other available study tasks. The development of usable, customizable, and secure informatics tools will become increasingly important as large genomic repositories begin to return research results at scale. Our work provides a proof-of-concept for developing and using such tools to aid in managing the return of results process within a national biobank.

Neonatal Intensive Care Workflow Analysis Informing NEC-Zero Clinical Decision Support Design.

Decision support in neonatal ICUs is needed, especially for prevention and risk awareness of the devastating complication of necrotizing enterocolitis, a major cause of emergency surgery among fragile infants. The purpose of this study was to describe the current clinical workflow and sociotechnical processes among clinicians for necrotizing enterocolitis risk awareness, timely recognition of symptoms, and treatment to inform decision support design. A qualitative descriptive study was conducted. Focus groups were held in two neonatal ICUs (five groups in Unit A and six in Unit B). Transcripts were analyzed using content analysis and compared with field notes. Clinicians (N = 27) included nurses (37%), physicians (30%), neonatal nurse practitioners (19%), and other staff (16%). Workflow processes differed for nurses (who see necrotizing enterocolitis signs and notify providers to order diagnostic tests and treatments) and providers (who receive notification of necrotizing enterocolitis concern and then decide how to act). Clinicians desired (1) a necrotizing enterocolitis-relevant dashboard to support nutrition tracking and necrotizing enterocolitis recognition; (2) features to support decision-making (eg, necrotizing enterocolitis risk and adherence scoring); (3) breast milk tracking and feeding clinical decision support; (4) tools for necrotizing enterocolitis surveillance and quality reporting; and (5) general EHR optimizations to improve user experience.

Addressing the Challenges of Penicillin Allergy Delabeling With Electronic Health Records and Mobile Applications

Allergy labels are common, often incorrect, and potentially harmful. There are many opportunities for clinical decision support (CDS) tools integrated in the electronic health record (EHR) and mobile apps to address the challenges with drug allergy management, including penicillin allergy delabeling (PADL). Effective delabeling solutions must consider multidisciplinary clinical workflow and multistep processes, including documentation, assessment, plan (eg, allergy testing and referral), record update, drug allergy alert management, and allergy reconciliation over time. Developing a systematic infrastructure to manage allergies across the EHR is critical to improve the accuracy and completeness of a patient's allergy and avoid inadvertently relabeling. Improving the appropriateness and relevancy of drug allergy alerts is important to reduce alert fatigue. Using alerts to guide clinicians on appropriate antibiotic use may reduce unnecessary β-lactam avoidance. To date, EHR CDS tools have facilitated non-allergists to provide PADL at the point of care. A mobile app was shown to support PADL and provide specialist support and education. Future research is needed to standardize, integrate, and evaluate innovative CDS tools in the EHR to demonstrate patient safety and clinical utility and facilitate wider adoption.

The Accuracy of Multiparametric MRI in Identification of Intraprostatic Tumor Deposits after Local Prostate Radiation Therapy

<h3>Purpose/Objective(s)</h3> To determine the accuracy of multiparametric (mp) MRI in identification of intraprostatic tumor deposits in prostate cancer patients previously treated with local radiation therapy (RT) using a systematic and targeted MR-guided transperineal prostate biopsy technique as a reference to guide and improve clinical workflow processes. <h3>Materials/Methods</h3> Twenty-three patients with localized prostate cancer who had been previously treated with external beam RT, brachytherapy, or combined external beam RT and brachytherapy, underwent a combined systematic and targeted MRI-guided prostate biopsy using a transperineal approach under general anesthesia. For each patient, a pre-biopsy mpMRI scan was obtained and imported into a prostate biopsy planning system. Transverse T2-weighted images were then reoriented from the supine to dorsal lithotomy position. Dividing the prostate into an apical and base section, a systematic array of transperineal biopsies spaced approximately 10mm apart was planned with additional biopsies targeting any mpMRI-identified PI-RADS 3, 4, or 5 lesions. Biopsy procedures were carried out in the dorsal lithotomy position using a transrectal ultrasound with stepper-stabilizer and template grid. Matching of the planning mpMRI images to live ultrasound images was achieved using the template grid as a reference. <h3>Results</h3> All patients successfully underwent their biopsy procedure as planned. The median PSA prior to biopsy was 5.5 ng/mL (range 2.1-24.6). The median number of biopsy specimens obtained per patient was 21 (range 13-34). Overall, the positive predictive value (PPV) of any PI-RADS 3-5 lesion (based on pathologic confirmation) was 81% (21/26). The individual PPV of PI-RADS 3, 4 and 5 lesions were 25% (1/4), 89% (16/18), and 100% (4/4), respectively. Forty-eight percent (11/23) of patients harbored mpMRI-unidentified prostate cancer. Thirty-five percent (8/23) of patients harbored mpMRI-unidentified GS≥7 disease. <h3>Conclusion</h3> Using prostate mpMRI to identify intraprostatic tumor deposits after local radiation therapy results in a high PPV, particularly for PI-RADS 4 and 5 lesions. However, given the high rate of mpMRI-unidentified prostate cancer, the addition of systematic transperineal prostate biopsy information appears necessary to improve the accuracy of mpMRI identification of intraprostatic tumor deposits.

How can we Develop a Standard Interface that easily Integrates with Hl7, boosting EHR Interoperability?

Background: It appears that a systematic strategy that includes the basic underpinnings of health care, from systems and technologies to clinical understanding and workflow processes, is required to achieve the next great leap in improving quality and safety. To make meaningful progress, health-care systems integration is essential. However, to provide interoperability in EHR, systems must support all versions of HL7.Software companies and healthcare organisations are still having trouble integrating HL7 with multiple interfaces. Thus, a standard interface or platform is required that easily integrates with HL7 boosting EHR interoperability. Methods: Basic Modelling Interface (BMI) interoperability standards will be the focus of this research. The research plan proposes a qualitative research approach that includes clinicians’ interview and a panel discussion to characterise the information flow models. Qualitative research using a thematic analysis of semi structured interviews will be conducted in the proposed research plan. Results: Gaining the HL7 standard is insufficient for integrating EHR with diverse apps or software in use, thus clinicians' willingness to adopt a common interface is critical. Medical practitioners and software vendors agreement required to develop an API that tackles HL7 integration issues and EHR interoperability. Planning techniques for dealing with HL7 issues and doing coordinated testing can help identify and install HL7 standard interfaces successfully. Conclusions: The proposed research plan will help us to create a standard interface for HL7 integration which will benefit clinicians, healthcare professionals, and groups such as patients, healthcare authorities, and EHR vendors. Lastly, reducing or eliminating healthcare concerns will help the economy, ecosystem, and society in both concrete and qualitative ways.

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Transition to practice in radiation oncology: Mind the gap

IntroductionPhysicians entering independent practice often express apprehension in managing the non-clinical aspects of practice. This study examined the perceived preparedness of radiation oncology (RO) residents for independent practice, identified education gaps, and discussed how these deficiencies could be addressed. Materials and methodsFocus groups with senior RO residents, fellows, new-to-practice radiation oncologists (ROs), and residency program directors were conducted. Data were coded using the Canadian Medical Education Directives for Specialists (CanMEDS) competencies using thematic analysis. ResultsCommonly reported gaps in the transition to practice (TtP) for ROs were lack of experience with: practice management, understanding the structure and function of the health care system and how it varies by jurisdiction, financial planning, effective communication and collaboration with other health care team members, creation of accurate and timely documentation, and radiotherapy problem-solving related to treatment planning and evaluation. Suggestions to address these challenges included use of mentorship, educational resources, courses, simulation-based medical education, improved graded responsibility, resident longitudinal clinics, and formal curricula in radiation therapy planning and evaluation. ConclusionThere are gaps in TtP education for RO trainees with opportunities for enrichment through the forthcoming implementation of a competency-based medical education framework in 2019. The gap in perceived competency in physician-related radiotherapy tasks may be caused by the complex interaction of clinical workflow processes, people and technology that has led to ineffective integration of trainees. The data are informative to medical education leaders for the development of comprehensive TtP curricula.

Abstract P5-11-04: Technology as a change agent for improving breast cancer quality care

Abstract Background: With rapid advances in research, clinicians often struggle to remain current with evolving care guidelines and to implement current national quality standards (NQS) relevant to breast cancer management. Adherence to NQS is driving reimbursement for cancer services, but clinical workflow processes and IT solutions are lacking to effectively document adherence. The Carevive Care Planning SystemTM (CPS), an evidence-based, patient assessment and care planning software, is designed to close gaps in quality cancer care by marrying clinical and patient-reported data with evidence-based algorithms to help centers improve and document their adherence rates to quality care standards. Methods: This study enrolled 30 non-metastatic breast cancer patients presenting to an NCI-designated comprehensive cancer center for no greater than their second medical oncology visit, and compared provider adherence to quality metrics for these patients with 30 matched historical controls who were seen prior to the study intervention. All were planned for chemotherapy treatment. The two part study intervention included 1) Provider participation in certified continuing medical education (CME) on evidence-based assessment, decision-making, and management strategies for breast cancer and 2) Use of the Carevive CPS with intervention subjects, each of whom who completed a electronic survey assessing current symptoms and concerns prior to their visit, and then received a provider-approved care plan including tailored recommendations for symptom management and referrals. The primary aim was to compare provider adherence to select quality metrics between historical controls (pre-test) and post-intervention subjects. Analysis/Results: Patient enrollment began in July 2015 and an earlier report of control data showed improved provider knowledge post-CME and opportunities to improve adherence. Median age and distribution of race, ethnicity, breast cancer stage, and HER2/ER status was not statistically different between the groups. Provider adherence to quality standards from pre to post-test is shown below: Quality Standard MetricsQuality StandardNPrePostChi-squarepAssessed emotional well being6020%50%6.190.045Addressed emotional well being2133.3%93.3%8.510.004Pain quantified by second visit60100%100%N/AN/APain plan documented1137.533.3%&amp;lt;10.90Opioid assesssed post treatment2791.0%100%1.510.22Opioid induced constipation assessed2718.2%9.3%&amp;lt;10.33 Conclusions:Provider adherence to quality metrics for emotional wellbeing increased from pre- to post- intervention, but did not for pain assessment and management. This was largely due to ceiling effect, but opportunities exist for continued improvement in pain management, at least in documentation. The Carevive CPS plus CME has the potential to allow institutions an patient-centered and user-friendly approach to both improve and document adherence to quality metrics. Citation Format: Hathaway A, Stricker C, Halilova KI, Hammelef KJ, Wujcik D, Dudley WN, Rocque G. Technology as a change agent for improving breast cancer quality care [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-11-04.

Integrating a Touchscreen-Based Assessment and Screening Tool for Adults with Multiple Myeloma

Background: More than 60% of multiple myeloma (MM) diagnoses and nearly 75% of deaths occur in patients over 65 years old. Because older adults (OAs) experience more treatment-related toxicities, treatment disruptions or dose reductions may be based on age and performance status alone, despite their poor predictive value for patient outcomes. Comprehensive Geriatric Assessment (CGA), including frailty indices, has shown predictive validity for toxicity and survival in OAs with MM, but is not routinely used in practice due to time and complexity, a lack of clarity about optimal tools and technologies to implement them, and clinician knowledge gaps on how to incorporate CGA into decision-making and care.Purpose: This project aims to address these gaps by pilot testing a tablet-based modified (m)CGA in 120 patients that presents compiled CGA results, including a frailty score, back to clinicians interacting with patients at the time of a treatment decision. Outcomes include feasibility, usability, utility, and impact on treatment decision-making, from both patient and provider perspectives. Pre-study implementation processes and milestones, including development of the mCGA, clinical workflow planning processes, training and other site-initiation activities are presented herein.Methods: The mCGA was developed using an iterative and dynamic consensus-driven process that included: 1) literature review and expert input to identify CGA domains for potential inclusion and 2) consensus building within a multi-disciplinary panel of gero-oncology experts, nurse scientists, and psychometricians. Domains and measures were selected based on predictive ability, length, and ability to administer via patient self-report so as to reduce clinician assessment burden. Study training and implementation procedures were developed using the same approach, as well as through workflow analysis and clinical team consensus building at the participating sites.Results: The Palumbo frailty index (FI) was chosen as the core of the mCGA tool given correlation with clinical outcomes specifically in OAs with MM. In addition to the 4 mGA measures comprising the Palumbo FI (age, comorbidity, ADL, and IADL), other GA variables were also chosen based on their strong predictive ability, clinical feasibility, and relevance to the MM population. This summary of results is displayed for ease of provider use within the Carevive dashboard (see Figure 1). Given prevalent knowledge gaps in use of CGA for MM treatment decision-making and care, a certified medical education self-study course was developed for training prior to the study intervention. Four geographically-dispersed academic and community hospitals who treat high volumes of diverse MM patients are participating to date. All 4 sites developed a process for ensuring treating providers would have easy access to the platform.Conclusions: Real-world, comprehensive and innovative solutions, combining education, geriatric assessment (GA) tools to determine a patient's fit/frailty status, realistic clinical work flow processes, and technology tools are needed to support and enhance treatment-decision making for patients with MM as well as their providers. [Display omitted] DisclosuresWildes:Carevive Systems: Consultancy. Stricker:Carevive Systems, Inc.: Employment, Equity Ownership. Dudley:Carevive Systems, Inc.: Consultancy. Harris:Carevive Systems, Inc.: Consultancy. Nathwani:Carevive Systems, Inc.: Research Funding. Brant:Carevive Systems, Inc.: Research Funding. Kurtin:Carevive Systems, Inc.: Research Funding. Hurria:Boehringer Ingelheim Pharmaceuticals: Consultancy; GTx, Inc: Consultancy; Carevive: Consultancy; Celgene: Other: Research; Optum Health Care SOlutions: Consultancy, Other: Conference panel, research; Sanofi: Consultancy; Novartis: Other: Research.

Contralateral artery enlargement predicts carotid plaque progression based on machine learning algorithm models in apoE-/- mice.

BackgroundThis study specifically focused on anatomical MRI characterization of the low shear stress-induced atherosclerotic plaque in mice. We used machine learning algorithms to analyze multiple correlation factors of plaque to generate predictive models and to find the predictive factor for vulnerable plaque.MethodsBranches of the left carotid artery in apoE−/− and C57BL/6J mice were ligated to produce the partial left carotid artery model. Before surgery, and 7, 14, and 28 days after surgery, in vivo serial MRI measurements of carotid artery diameter were obtained. Meanwhile, proximal blood flow was evaluated. After image acquisition and animal sacrifice, carotid arteries were harvested for histological analysis. Support vector machine (SVM) and decision tree (DT) were used to select features and generate predictive models of vulnerable plaque progression.ResultSeven days after surgery, neointima formation was visualized on micro-MRI in both apoE−/− and C57BL/6J mice. Ultrasonography showed that blood flow had significantly decreased compared to that in the contralateral artery. Partial ligation of the carotid artery for 4 weeks in apoE−/− mice induced vulnerable plaque; however, in C57BL/6J mice this same technique performed for 4 weeks induced arterial stenosis. Contralateral carotid artery diameter at 7 days after surgery was the most reliable predictive factor in plaque progression. We achieved over 87.5% accuracy, 80% sensitivity, and 95% specificity with SVM. The accuracy, sensitivity, and specificity for the DT classifier were 90, 90, and 90%, respectively.ConclusionsThis study is the first to demonstrate that SVM and DT methods could be suitable models for identifying vulnerable plaque progression in mice. And contralateral artery enlargement can predict the vulnerable plaque in carotid artery at the very early stage. It may be a valuable tool which helps to optimize the clinical work flow process by providing more decision in selecting patients for treatment.

Advance Care Planning: Understanding Clinical Routines and Experiences of Interprofessional Team Members in Diverse Health Care Settings.

Interprofessional health care team members consider advance care planning (ACP) to be important, yet gaps remain in systematic clinical routines to support ACP. A clearer understanding of the interprofessional team members' perspectives on ACP clinical routines in diverse settings is needed. One hundred eighteen health care team members from community-based clinics, long-term care facilities, academic clinics, federally qualified health centers, and hospitals participated in a 35-question, cross-sectional online survey to assess clinical routines, workflow processes, and policies relating to ACP. Respondents were 53% physicians, 18% advanced practice nurses, 11% nurses, and 18% other interprofessional team members including administrators, chaplains, social workers, and others. Regarding clinical routines, respondents reported that several interprofessional team members play a role in facilitating ACP (ie, physician, social worker, nurse, others). Most (62%) settings did not have, or did not know of, policies related to ACP documentation. Only 14% of settings had a patient education program. Two-thirds of the respondents said that addressing ACP is a high priority and 85% felt that nonphysicians could have ACP conversations with appropriate training. The clinical resources needed to improve clinical routines included training for providers and staff, dedicated staff to facilitate ACP, and availability of patient/family educational materials. Although interprofessional health care team members consider ACP a priority and several team members may be involved, clinical settings lack systematic clinical routines to support ACP. Patient educational materials, interprofessional team training, and policies to support ACP clinical workflows that do not rely solely on physicians could improve ACP across diverse clinical settings.

Abstract P1-10-16: Improving provider adherence to breast cancer care quality metrics: Use of a novel care planning tool

Abstract Background: With rapid advances in research clinicians often struggle to remain current on evolving care guidelines as well as current National Quality Standards (NQS) relevant to breast cancer management. Adherence to NQS now drives much reimbursement for cancer center services but clinical workflow processes and IT solutions are often not in place to effectively document adherence. The On Q Care Planning SystemTM (CPS), an evidence-based patient assessment and care planning software, has been designed to close gaps in quality cancer care and facilitate data collection to help centers both better understand and document their adherence rates to quality care standards. Methods: This multi-site study will enroll approximately 150 non-metastatic breast cancer patients, presenting for no greater than their second medical oncology visit, across five cancer centers. Patients must be planned for but not yet receiving chemotherapy treatment. A between subject design using 150 matched historical controls will be used to assess the impact of the 2-part intervention, at both the patient and provider level, on select quality metrics. At two consecutive clinical visits, patients will engage with the On Q CPS to assess family and medical history and current symptoms and receive two separate care plans. Care plans include (when applicable) recommendations for symptom management and appropriate referrals (i.e. genetic counseling for those at increased hereditary risk, and/or reproductive endocrinology for those interested in preserving fertility). To augment the effectiveness of the On Q CPS, providers will also participate in certified continuing medical education activities designed to educate about evidence-based assessment, decision-making, and management strategies for breast cancer patients. The primary aim is to evaluate provider adherence to select quality metrics among recently diagnosed breast cancer patients following the intervention, and compare to adherence rates for historical controls from the pre-intervention period. Metrics of primary interest include distress screening and management, complete family history assessment, genetic counseling referral, discussion of infertility risk, and discussion of fertility preservation options and/or referral to a specialist. These metrics have been chosen as the primary endpoints given that they have been historically documented as being resistant to change. Outcomes will be assessed by chart abstraction using a score card method of select quality metrics for both enrolled patients and matched historical controls. Analysis/Results: Patient enrollment begins in June 2015 and thus data will be presented at time of symposium. Patient characteristics and primary outcomes will be analyzed using a multi-step approach to first describe and then compare, at the individual patient level, provider adherence to the select quality metrics evaluated in this study. Descriptive statistics will also be estimated within a mixed model approach for the rate with which each single metric was achieved across patients. Conclusions: The On Q CPS, a care planning software tool, has the potential to both improve provider adherence to NQS and allow institutions an easy and accessible way to document that adherence. Citation Format: Hathaway A, Halilova K, Gaguski ME, Thomas K, Dudley WN, Stricker CT, Hammelef KJ, Panzer SL, Rocque GB. Improving provider adherence to breast cancer care quality metrics: Use of a novel care planning tool. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P1-10-16.

A Mixed-Methods Research Framework for Healthcare Process Improvement

The healthcare system in the United States is spiraling out of control due to ever-increasing costs without significant improvements in quality, access to care, satisfaction, and efficiency. Efficient workflow is paramount to improving healthcare value while maintaining the utmost standards of patient care and provider satisfaction in high stress environments. This article provides healthcare managers and quality engineers with a practical healthcare process improvement framework to assess, measure and improve clinical workflow processes. The proposed mixed-methods research framework integrates qualitative and quantitative tools to foster the improvement of processes and workflow in a systematic way. The framework consists of three distinct phases: 1) stakeholder analysis, 2a) survey design, 2b) time-motion study, and 3) process improvement. The proposed framework is applied to the pediatric intensive care unit of the Penn State Hershey Children's Hospital. The implementation of this methodology led to identification and categorization of different workflow tasks and activities into both value-added and non-value added in an effort to provide more valuable and higher quality patient care. Based upon the lessons learned from the case study, the three-phase methodology provides a better, broader, leaner, and holistic assessment of clinical workflow. The proposed framework can be implemented in various healthcare settings to support continuous improvement efforts in which complexity is a daily element that impacts workflow. We proffer a general methodology for process improvement in a healthcare setting, providing decision makers and stakeholders with a useful framework to help their organizations improve efficiency.

Pre-Implementation Case Studies Evaluating Workflow and Informatics Challenges in Private Primary Care Clinics for Electronic Medical Record Implementation

Despite the potential benefits of electronic medical records (EMRs), their implementations have failed in a number of instances because of misfit between the technology design, work practices, workflows, and clinicians' needs. To promote effective design and implementation of an EMR system, the present pre-implementation study modeled the clinical workflow processes in private primary health care clinics and identified the associated gaps, weaknesses, and informatics challenges in the processes. Systematic field observations were performed to collect workflow data. Forty-three health care professionals (16 physicians and 27 clinical assistants) of fifteen private primary health care clinics were studied. The results, presented in a workflow diagram, demonstrate step-by-step details of the clinical work processes, their gaps and weaknesses, and possible improvement opportunities offered by the application of EMR. The workflow model provides important implications that may be applicable to other health care settings that plan to implement EMRs.

Human factors and health information technology: current challenges and future directions.

Recent federal mandates and incentives have spurred the rapid growth, development and adoption of health information technology (HIT). While providing significant benefits for better data integration, organization, and availability, recent reports have raised questions regarding their potential to cause medication errors, decreased clinician performance, and lowered efficiency. The goal of this survey article is to (a) examine the theoretical and foundational models of human factors and ergonomics (HFE) that are being advocated for achieving patient safety and quality, and their use in the evaluation of healthcare systems; (b) and the potential for macroergonomic HFE approaches within the context of current research in biomedical informatics. We reviewed literature (2007-2013) on the use of HFE approaches in healthcare settings, from databases such as Pubmed, CINAHL, and Cochran. Based on the review, we discuss the systems-oriented models, their use in the evaluation of HIT, and examples of their use in the evaluation of EHR systems, clinical workflow processes, and medication errors. We also discuss the opportunities for better integrating HFE methods within biomedical informatics research and its potential advantages. The use of HFE methods is still in its infancy - better integration of HFE within the design lifecycle, and quality improvement efforts can further the ability of informatics researchers to address the key concerns regarding the complexity in clinical settings and develop HIT solutions that are designed within the social fabric of the considered setting.

Medical errors: Moral and ethical considerations

Medical errors are an inevitable part of the practice of medicine and pose a significant threat to the safety of patients. Improper management of healthcare professionals, clinical workflow processes, and information systems often leads to medical errors. Disclosure of medical errors to patients and family members serves as a catalyst for litigation and thus, the establishment of appropriate moral and ethical standards cannot be ignored when implementing solutions to remedy the situation. In this paper, a systematic literature review was used to explore and evaluate the moral and ethical issues that confront the healthcare industry as a result of medical errors. The practical implications of medical errors were also discussed.

Improving laboratory data entry quality using Six Sigma

The Uganda Makerere University provides clinical laboratory support to over 70 clients in Uganda. With increased volume, manual data entry errors have steadily increased, prompting laboratory managers to employ the Six Sigma method to evaluate and reduce their problems. The purpose of this paper is to describe how laboratory data entry quality was improved by using Six Sigma. The Six Sigma Quality Improvement (QI) project team followed a sequence of steps, starting with defining project goals, measuring data entry errors to assess current performance, analyzing data and determining data-entry error root causes. Finally the team implemented changes and control measures to address the root causes and to maintain improvements. Establishing the Six Sigma project required considerable resources and maintaining the gains requires additional personnel time and dedicated resources. After initiating the Six Sigma project, there was a 60.5 percent reduction in data entry errors from 423 errors a month (i.e. 4.34 Six Sigma) in the first month, down to an average 166 errors/month (i.e. 4.65 Six Sigma) over 12 months. The team estimated the average cost of identifying and fixing a data entry error to be $16.25 per error. Thus, reducing errors by an average of 257 errors per month over one year has saved the laboratory an estimated $50,115 a year. The Six Sigma QI project provides a replicable framework for Ugandan laboratory staff and other resource-limited organizations to promote quality environment. Laboratory staff can deliver excellent care at a lower cost, by applying QI principles. This innovative QI method of reducing data entry errors in medical laboratories may improve the clinical workflow processes and make cost savings across the health care continuum.

A decision methodology for managing operational efficiency and information disclosure risk in healthcare processes

This paper addresses two critical challenges faced by healthcare organizations: significant personnel shortages and mandates to safeguard patient safety and information security. We develop a two-stage decision making methodology to optimize the healthcare workflow task assignments and mitigate information disclosure risks. While the first stage throughput optimization formulation maximizes operational efficiencies, it can expose organizations to information disclosure risks that can be exploited to violate patient safety and information security. To address the ensuing privacy and fraud concerns we define task-based conflict sets to assess disclosure risks with optimal task assignments. In the second stage of the solution methodology, various security control strategies – task based and employee based – are incorporated into a decision support model to help decision makers to effectively manage and achieve workflow efficiency and meet information security requirements. For practical settings where certain parameters are not obtainable or the problem is computationally intractable, we provide a sequential-decision approach that could yield approximate partial solutions. We conduct an extensive computational analysis of a clinical workflow process to illustrate the practical benefits of the proposed methodology.

Mitigating Error Vulnerability at the Transition of Care through the Use of Health IT Applications

Adverse drug events are largely considered to be errors in which the severity of effects could be lessened or even prevented through more effective medication reconciliation practices. Transitions of care, particularly at the time of discharge from the hospital, represent a time of heightened error vulnerability that contributes to medication discrepancy occurrences. The observed vulnerability can be attributed to communication and care continuity gaps across health care settings and can often lead to preventable errors. Health IT tools developed through research can identify factors which increase the risk of medication discrepancies. Additionally, the implementations of optimized clinical workflow processes to form effective transitions of care are approaches to decreasing medication discrepancies which may lead to adverse drug events. While federal policies and certifying organizations have implemented quality initiatives to increase focus on medication reconciliation practices in the hospital and primary care settings, the same practices must be implemented after a patient is discharged to their homes or another health care facility in order to mitigate error vulnerabilities that occur at the transition of care. This paper provides an overview of health IT system capabilities and their applications within and across health care delivery settings to facilitate care coordination to ensure continuity of care.