Prescription Plan Research Articles

PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases

Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires further exploration. In this study, we proposed a TCM herbal prescription planning framework based on deep reinforcement learning for chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal prescription optimization model that focuses on long-term effectiveness rather than achieving maximum reward at every step, thereby ensuring better patient outcomes. We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark. Our results showed that PrescDRL achieved a higher curative effect, with the single-step reward improving by 117% and 153% compared to doctors. Furthermore, PrescDRL outperformed the benchmark in prescription prediction, with precision improving by 40.5% and recall improving by 63%. Overall, our study demonstrates the potential of using artificial intelligence to improve clinical intelligent diagnosis and treatment in TCM.

A framework for conceptualizing integrated prescriptive maintenance and production planning and control models

Goal: This study aims to support researchers and managers in conceptualising new integrated prescriptive maintenance (PxM) and production planning and control (PPC) models. Design / Methodology / Approach: We perform a systematic literature review based on Thomé et al. (2016) and analyse literary findings using qualitative content analysis and quantitative correlation analyses. Results: This work identifies 94 integrated PxM and PPC planning models and 47 outcomes, 16 decision variables and 34 environment entities. Based on the quantitative analyses of these components, we derive a normative framework to guide researchers and practitioners in conceptualising integrated models. Limitations of the investigation: The study is limited to only one scientific database. Additionally, the quantitative analyses might be sensitive due to a low sample size for some components, and we only measure the linear dependency between two components. Lastly, we do not address solution algorithms. Practical implications: The framework constitutes a tool for managers to construct integrated models tailored to their specific planning problems, fostering alignment between production and maintenance departments, plans and controls. Originality / Value: We provide a descriptive overview and normative guidance in the selection of components that can or should be used for future PxM-aligned PPC planning studies, pinpointing possible research gaps.

Joint Prescriptive Maintenance and Production Planning and Control Process Simulation for Extrusion System

Production planning and control (PPC) is the mainstay of every manufacturer and ensures flawless production processes. However, PPC is jeopardized by breakdowns that can only be tackled with appropriate maintenance. In the past, static strategies, such as reactive and scheduled maintenance, have been used. Yet, with growing system complexity, Industry 4.0, and abundant sensor data, dynamic strategies through PHM have emerged. The most advanced maintenance strategy is prescriptive maintenance (PxM), which allows manufacturers not only to predict failures but also to establish condition-based production plans and controls. To this end, our study explores the integration of PxM with PPC. First, we propose a fault prediction model based on health indicators and future loads of a single-machine system. The proposed fault prediction is integrated into a joint PxM and PPC simulation model that compares the make­span of three joint PxM and PPC strategies inter se and versus reactive and scheduled maintenance. A simulation study using industrial data from an extrusion process evaluates the different strategies across different time horizons (one month to a year). The findings indicate that joint PxM and PPC outperform other strategies, providing significant time savings over traditional methods. Further, a sensitivity analysis is conducted to assess the robustness of the PxM strategies under varying levels of measurement noise, revealing potential challenges under high noise conditions. The study contributes to the field of PHM by providing insights into the effectiveness of joint PxM and PPC strategies and offering a comprehensive analysis of their performance under different conditions.

Combining zip code-based population data and pharmacy administrative claims data to create measures of social determinants of health.

Social determinants of health (SDoH) are key factors that impact health outcomes. However, there are many barriers to collecting SDoH data (eg, cost of data collection, technological barriers, and lack of standardized measures). Population data may provide an accessible alternative to collecting SDoH data for patients. To explain how population data can be leveraged to create SDoH measures, assess the association of population SDoH measures with diabetic medication adherence, and discuss how understanding a patient's SDoH can inform care plans and patient engagement. A nationally representative commercial sample of patients who were aged 18 years and older and met Pharmacy Quality Alliance inclusion criteria for diabetes mellitus were analyzed (N = 37,789). US Census and North American Industry Classification System data were combined with pharmacy administrative claims data to create SDoH measures. Derived measures represent 2 SDoH domains: (1) economic stability (housing density, housing relocation, jobs per resident, and average salary) and (2) health care access and quality (urban/rural classification, distance traveled to prescriber and pharmacy, use of a primary care provider [PCP], and residents per PCP). The association of population SDoH measures with diabetic medication adherence (proportion of days covered) was assessed via logistic regression, which included covariates (eg, sex, age, comorbidities, and prescription plan attributes). As housing density (houses per resident) increased, so did the likelihood of adherence (odds ratio = 1.54, 95% CI = 1.21-1.97, P = 0.001). Relative to patients who did not move, patients who moved once had 0.87 (95% CI = 0.81-0.93, P < 0.001) the odds of being adherent, and patients who moved 2 or more times had 0.82 (95% CI = 0.71-0.95, P = 0.008) the odds of being adherent. Compared with areas with fewer jobs per resident, patients living within a zip code with 0.16 to 0.26 jobs per resident were 1.12 (95% CI = 1.04-1.20, P = 0.002) times more likely to be adherent. Patients who lived in an urban cluster were 1.11 (95% CI = 1.01-1.22, P = 0.037) times more likely to be adherent than patients living in a rural area. Patients who travel at least 25 miles to their prescriber had 0.82 (95% CI = 0.77-0.86, P < 0.001) the odds of being adherent. Community pharmacy users had 0.65 (95% CI = 0.59-0.71, P < 0.001) the odds of being adherent compared with mail order pharmacy users. Patients who had a PCP were 1.26 (95% CI = 1.18-1.34, P < 0.001) times more likely to be adherent to their medication. Leveraging publicly available population data to create SDoH measures is an accessible option to overcome barriers to SDoH data collection. Derived measures can be used to increase equity in care received by identifying patients who could benefit from assistance with medication adherence.

Role of Radiotherapy Technologists in Clinical Radiation Therapy Practices: A Comprehensive Review

Radiation therapists, also known as radiotherapy technologists, are allied health professionals who specialise in providing high doses of radiation to patients with malignant growths. High-energy radiation damages cells’ genetic material {Deoxyribonucleic Acid (DNA)}, stopping them from replicating and multiplying further. Radiation can be used to treat cancer or as a highly effective palliative therapy to alleviate cancer-related pain in patients. Radiation therapy can be used in conjunction with other treatments, including surgery, chemotherapy, and immunotherapy. Because radiation harms both healthy tissues and malignant cells, the primary objective of radiation therapy is to optimise radiation exposure to abnormal cancer cells while avoiding irradiation of healthy tissues adjacent to or in the path of radiation. Remarkable precision is required to provide ionising radiation to patients and target specific cells for consistent, accurate, and successful radiation treatment. A team consisting of radiation oncologists, medical physicists, radiation therapists, medical dosimetrists, and nurses work together to achieve this goal. Radiation therapists are responsible for delivering radiation treatment, as well as maintaining and testing the equipment used to monitor and administer the treatment. The radiation therapist administers the patient’s treatment dose according to the prescription plan made by the radiation oncologists and medical physicists. Radiation therapists must communicate with patients and their families, respond to their inquiries, and clarify the radiation treatment plan. They must also track the patient’s progress and report any changes to their seniors. Radiation therapists must maintain high levels of precision while administering therapy, think critically, and exercise autonomous, professional, and ethical judgment in all aspects of their work. The field of radiotherapy is continuously evolving with new advancements in technology and treatment delivery techniques that maximise patient satisfaction and treatment delivery efficacy. Radiation therapists undergo continuing education and training to broaden their knowledge of the field and stay current with standard practice. This review addresses the critical role of radiation therapists in clinical radiation therapy practices. It highlights their significance in delivering safe and effective treatment to radiotherapy patients. The review covers various aspects, including the responsibilities of technologists in treatment planning, equipment operation, patient care and safety, quality assurance, and research. This present review provides a comprehensive understanding of the importance of well-trained and skilled radiation therapists in ensuring the success of radiation therapy treatments.

Failed pedestrian street experiments in high-density urban Asia: A matter of policies?

Promoted by sustainable urban agendas and urged by global pandemic measures, street experiments (SE) are booming in Europe but remain latent in Asia. These experiments aim to reconfigure streets as more than spaces for motorized traffic movements, enabling a temporary urban paradigm shift. Such a shift involves balancing active mobility and public space uses in streets while envisioning radically different settings and uses. Recently, eminent scholars urged considering SE in connection to the system and planning framework within which SE are conceived to trace their trajectories. This article examines four decades of temporary-pedestrianization policies and planning instruments in Hong Kong, an Asian city representative of high-density urban environments with highly intensive use of road space and conservative and prescriptive planning. In doing so, the article identifies four trajectories and illustrates two emblematic cases: Chater Road, the first street temporarily pedestrianized under a commercial initiative, and Sai Yeung Choi Street South, a street pedestrianized under a government initiative, now turned back to its original function. The competing roles and practical uses that pedestrianized streets must fulfil partially determined their fate. However, the trajectories these cases followed also differ due to the contextual planning approach and decision-making process. The study contributes to scholarship on SE by shedding new light on the geographical context of high-density urban Asia, forwarding challenges that policymakers might need to address in the planning and governance of SE in similar environments.

Planning of prescriptive maintenance types for generator with fuzzy logic-based genetic algorithm in a hydroelectric power plant

The move to predictive and prescriptive maintenance underlines the need for cost-effective methods to monitoring degradation and predicting failures. It is in hydroelectric power plants (HPPs) where the most powerful and unique techniques of this evolution were observed. The final premise of this transition is the need to improve the decision-making process for planning activities in the Prescriptive Maintenance Planning (MP). This encourages the use of fuzzy set theory in process management. Although there are many studies on MP in the literature, these studies do not fully reflect the real life as they ignore potential schedule delays and the indirect impact of sustainable strategy on revenues. Therefore, in the first stage of this study, it is aimed to weight the maintenance types (MTs) on a plant basis for the generator, which is the most critical system of the pumped storage HPP, and in the second stage, it is aimed to create MPs that minimize delays due to system shutdowns with weighted MTs. For this purpose, in the first stage, the MTs for the HPP were weighted by AHP-TOPSIS combination augmented with Pythagorean fuzzy numbers, and in the second stage, a Genetic Algorithm model was designed for the problem of coordinating maintenance durations and deadlines in addition to these weights. Averaged over 50 iterations, the average number of delayed MPs is 142.55, indicating that the algorithm is successful in reducing delayed MPs, while the average weighted delay value is 172.85, indicating that more work is needed in terms of weighted delay.

Drug use and spending under a formulary informed by cost-effectiveness.

BACKGROUND: The National Academy of Medicine has called for value-based drug formularies to address health plan prescription drug spending while maintaining access to high-value medicines. Thirty employer-sponsored plans implemented a "Value-Based Formulary-essentials" (VBF-e) program that uses cost-effectiveness evidence to inform cost-sharing and coverage exclusion. OBJECTIVE: To evaluate if the VBF-e was associated with changes in medication use and patient out-of-pocket spending and health plan spending on prescription drugs and other health care. METHODS: This was a cohort study using a difference-in-differences design from 2015 through 2019 with 1 year of follow-up after VBF-e implementation at Premera Blue Cross, the largest nonprofit health plan in the Pacific Northwest. The VBF-e exposure group was composed of all individuals aged younger than 65 years and enrolled at least 12 months prior to their employer group's VBF-e implementation date. The contemporaneous control group was composed of propensity score-matched individuals with the same inclusion criteria but their employer group that did not implement VBF-e. We prespecified the following outcomes: days of medication on hand overall and by VBF-e tier (high-value generic, brand, and specialty drugs were in tiers 1 to 3, respectively, and low-value drugs were in tier 4 or excluded from coverage); prescription drug spending; and other health care use (emergency department visits, hospital days, and outpatient visits). RESULTS: Comparing 12,111 exposed (mean age = 36.0; 49.8% female sex) participants with 24,222 control participants (mean age = 34.7; 49.6% female sex), VBF-e reduced use of low-value drugs by 0.3 days per member per month (PMPM) (95% CI = -0.5 to -0.1; 17% decrease) for tier 4 drugs and 0.4 days PMPM (95% CI = -0.5 to -0.4; 83% decrease) for excluded drugs. High-value specialty drug use increased by 0.1 days PMPM (95% CI = 0.0-0.1; 123% increase). Health plan spending decreased by $14 PMPM (95% CI = -26 to -4) and member out-of-pocket spending increased by $1 PMPM (95% CI = 1-2). Other health care use did not change significantly. CONCLUSIONS: An exclusion formulary informed by cost-effectiveness evidence reduced low-value drug use, increased high-value specialty drug use, reduced health plan spending, and increased member out-of-pocket spending without increasing acute care use. DISCLOSURES: This research was supported by a grant from the Patrick and Catherine Weldon Donaghue Medical Research Foundation's Greater Value Portfolio Program. Study Registration Number: NCT04904055.

Outside-in: Co-production and the spatial planning systems in Italy and England

This article concerns the growing interest in ‘co-production’ in spatial planning, focusing on its relationship with planning systems. The article refers to Italy and England – two institutional contexts with different planning systems – and concludes that co-production can operate outside or inside the planning system. Both models have pros and cons. However, the critical factor determining co-production regarding the planning system appears to be related to how land use rights are allocated. While prior allocation through prescriptive plans keeps co-production out of the system, only allocation as a final case-by-case decision allows co-production to be part of it.

KV-CBCT-Guided Daily Adaptation Improves Target Coverage and Spares Normal Tissue for Accelerated Partial Breast Irradiation

Linear accelerator based accelerated partial breast irradiation (APBI) in early-stage breast cancer necessitates a reduction in non-target breast tissue to decrease long term toxicity and adverse cosmetic outcome. In particular, lumpectomy cavity seromas may decrease in size and deform in shape during the course of short course APBI. Online adaptive RT (oART) offers considerable prospect for treating mobile targets through pendulous breast anatomy. However, there are limited studies outlining the methods and outcomes of CBCT-based oART for APBI. Here we present a retrospective, single institutional study analyzing the treatment process for patients receiving stereotactic kV-CBCT guided oART APBI. Fourteen patients were treated to 30 Gy in 5 fractions for a total of 70 fractions. Time between simulation and treatment, change in gross tumor volume (GTV), and differences in DVH metrics with adaption were analyzed. The Wilcoxon paired, non-parametric test was utilized to test for DVH metric differences between the scheduled plans (initial plans recalculated on daily CBCT anatomy) and treated plans, which were either the scheduled or adapted plan (initial plans re-optimized using daily anatomy), depending on the preference of the treating physician or physicist. Median (inter-quartile range) time from simulation to first treatment was 28 days (21-33 days). During this same time, GTV volume reduced to 72.5% (57.8-87.3%) of the simulation lumpectomy cavity volume. Adaptive treatments required 31.1 min (27.2-37.1 min) from start of CBCT to treatment session end. Table 1 summarizes differences between scheduled and treated plan metrics for 70 fractions, 62 (89%) of which were treated adaptively and 8 (11%) of which were treated using the scheduled plan. Significant improvement in prescription planning target volume coverage (p = 0.003), significant reduction in 5/6 organ-at-risk metrics evaluated (p ≤ 0.003), and significant improvement in conformity index and high dose spillage (p ≤ 0.001) were realized with adaption. Table 1: Scheduled versus treated plan metrics. APBI using oART decreased most organs at risk DVH metrics, improved plan quality metrics, and increased target coverage, justifying the use of kV-CBCT-guided oART for APBI.

Mitigation of IMRT/SBRT Treatment Planning Errors on the First Biology-Guided Radiotherapy System Using FMEA within Six Sigma Framework

Utilize the Six Sigma methodology and Failure Mode and Effect Analysis (FMEA) to prevent and mitigate errors in IMRT/SBRT treatment planning with the first clinical installation of biology-guided radiotherapy (BgRT) system. The Six Sigma approach consisted of five phases: Define-Measure-Analyze-Improve-Control. The Define-Measure-Analyze phases consisted of process mapping and an FMEA of the IMRT/SBRT treatment planning process on the BgRT system. The multidisciplinary team outlined the workflow process and identified the failure modes associated with the plan check items using AAPM TG-100 recommendations. Items with the highest average risk priority numbers (RPN) and Severity ≥7 were prioritized for automation using the treatment planning system scripting API (ESAPI). The Improve phase consisted of developing ESAPI scripts prior to the launch of the BgRT system to improve efficiency and safety. In the Control phase, the FMEA ranking was re-evaluated 1-year post-clinical launch. Overall, 100 plan check items were identified where the RPN values ranged from 10.2 to 429.0. Fifty of these items (50%) were suitable for automation within ESAPI. Of the 10 highest-risk items (Table 1), 8 were suitable for automation. Based on the results of the FMEA, two scripts were developed: Planning Assistant used by the planner during preparation for planning and the Automated Plan Check used by the planner and the plan checker during plan preparation for treatment. At 1-year post-clinical launch, the scripts were used for 80 patients successfully treated in 1747 fractions. During this period only 3 errors were reported: omitted bolus during treatment, nomenclature error in the BgRT system plan prescription, and dose tracking plan not approved following physics plan check. The average RPN pre-scripts was 138.0 compared to the average post-scripts RPN of 47.8 (p < 0.05) signifying a safer process. Implementing new technology into the clinic can be an error-prone process where the likelihood of errors increases with increasing pressure to implement the technology quickly. To limit errors in the clinical implementation of the first BgRT system, the Six Sigma methodology was utilized to identify failure modes, establish quality control checks, and re-evaluate these checks 1-year post-clinical launch.

S11-4 Physical activity prescription in the municipality of Camarena in Castilla-La Mancha, Spain

Abstract Purpose A pilot study of the Physical Activity (PA) Prescription (PAP) Plan in the municipality of Camarena (Castilla-La Mancha - CLM, Spain) was conducted to evaluate the incorporation of the exercise units which work together with General Practitioner (GP) to make “PA practice the easy option”. Target group is inactive people with or without chronic disease. Project or policy description The High Council for Sports of Spain launched a national PAP plan developed by the creation of exercise units that will work together with PA referral (PAR) in all autonomic Communities in Spain. The plan is using digital tools to improve the accessibility and efficacy of the Community Sport and Health systems to provide PA for the inactive population with or without chronic disease. This investment is part of the Sports Sector Digitization Plan, one of the three axes of the Component 26, of the Recovery, Transformation and Resilience Plan, aimed at promoting the sports sector. A pilot study in PAP was implemented in Camarena designed by PAFS research group from the University of CLM. The pilot study started with a local meeting including all agents in the community: local nurses and general practitioners (GP), all staff involved in the community PA and Sports offer, and town hall members. The pilot study included: (i) developing a both-ways communication system between GP practice and the exercise unit, (ii) developing a Physical Fitness and Health assessment battery tool for all patients, (iii) developing individualized exercise programs using digital tools, (iv) analyzing all opportunities to be physically active at the community, (v) quantitative and qualitative evaluation analyses of the implementation of the PAP in Camarena. This pilot study aimed to be the seed to scale-up PAP plan in CLM region. Conclusions The PAP plan had a good acceptance by the community of Camarena. The response rate of patients was high (85% of referred patients came to the exercise unit). Patients’ adherence to the exercise program was excellent. An educational plan is now developed as a policy-implication, for nurses, doctors, and PA educators in CLM, including all outcomes from PAP plan in Camarena.

Fringe Benefits Effects on Employee Productivity in the Public Sector Tamilnadu Water Supply and Drainage Board Namakkal

The research purpose is to determine the study of the fringe benefits important of employees. Fringe benefits are additions to compensation that companies give their employees. This research project is on Fringe Benefits and Employees productivity in public sector. This research work is generally about the Benefits and Employees productivity Public Sector. The project has undertook the general introduction into the research work led to the review of various literature that relates to the major variables involved in the research work especially employees productivity. The purpose of employee benefits is to increase the economic security of staff members, and in doing so, improve worker retention across the organization. As such, it is one component of reward management. In any case, employers use fringe benefits to help them recruit, motivate, and keep high-quality people. According to Mathis and John (2003), productivity is a measure of the quantity and quality of work done, considering the cost of the resources used. The more productive an organization, the better its competitive advantage, because the costs to produce its goods and services are lower. Employee benefits in Canada usually refer to employer sponsored life, disability, health, and dental plans. Employee benefits in the United States include relocation assistance; medical, prescription, vision and dental plans; health and dependent care flexible spending accounts; retirement benefit plans (pension, 401(k), 403(b). fringe benefits refers to the regular review of an employee’s job performance and overall contribution to a company. The objective is to know the effect of fringe benefits on employee motivation. The reveals that fringe benefits lead to improved employees’ performance. This results from increased productivity in the organization. The majorities of the employees are motivated of the organization through feedback and increased productivity.

Training Direct Care Staff to Empower Adults with Intellectual Disabilities and Their Families to Reduce Overmedication

ObjectivesPeople with intellectual and developmental disabilities (IDD) and autism are subject to restrictive practices like physical restraint and the overuse of psychotropic medications for challenging behavior. People with disabilities and their families are often not involved in shared decision-making for prescription and other care planning, which may violate the UN Human Rights Charter.MethodsOne way of reducing overmedication is by empowering adults with IDD and their family caregivers to collaborate in the decision to use medications. SPECTROM (https://spectrom.wixsite.com/project), a training program for direct care workers, was developed to help reduce the overmedication of these people. This article presents ideas on how SPECTROM resources could empower adults with IDD and their families to influence their care decisions, including psychotropic prescribing.ResultsInformation provided in 32 easy-read leaflets in SPECTROM on psychotropic medications can be used to improve shared decision-making involving people with IDD and their families. Similarly, the Yellow Passport, an easy-read health record, could help information sharing among care service providers, thus improving their health care. SPECTROM has one module devoted to effectively engaging with people with IDD to improve their communication and help them with their skills building.ConclusionsCurrently, very limited evidence exists on the effects of SPECTROM to empower individuals with IDD and their families in reducing psychotropic medications for challenging behaviors. In addition to the two pilot studies on SPECTROM from UK and Australia, further research is needed to explore the evidence supporting the above suggestions.

Industry 4.0 Maturity Models to Support Smart Manufacturing Transformation: A Systematic Literature Review

With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0 (I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state. Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart technology, products and services have been treated as the popular evaluation dimensions which will define the state of an industry’s maturity level.

Early Stent Patency After Endovascular Therapy for Tandem or Isolated Carotid Steno-Occlusive Disease

Early Stent Patency After Endovascular Therapy for Tandem or Isolated Carotid Steno-Occlusive Disease

AMPREDICT PROsthetics—Predicting Prosthesis Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning

ObjectiveTo develop and validate a patient-specific multivariable prediction model that uses variables readily available in the electronic medical record to predict 12-month mobility at the time of initial post-amputation prosthetic prescription. The prediction model is designed for patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation because of complications of diabetes and/or peripheral artery disease. DesignMulti-methodology cohort study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected their patient-reported mobility. SettingThe VA Corporate Data Warehouse, the National Prosthetics Patient Database, participant mailings, and phone calls. ParticipantsThree-hundred fifty-seven veterans who underwent an incident dysvascular TT or TF amputation and received a qualifying lower limb prosthesis between March 1, 2018, and November 30, 2020 (N=357). InterventionsNot applicable. Main Outcome MeasureThe Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6). ResultsMultinomial logistic lasso regression, a machine learning methodology designed to select variables that most contribute to prediction while controlling for overfitting, led to a final model including 23 predictors of the 4-category AMPSIMM outcome that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation—levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively. ConclusionsThe AMPREDICT PROsthetics model can assist providers in estimating individual patients’ future mobility at the time of prosthetic prescription, thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for anticipated functional goals.

AMPREDICT PROsthetics- Predicting Prosthetic Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning

Research Objectives To develop and validate a patient-specific multivariable prediction model that utilizes readily available predictors to predict 12-month mobility at the time of initial post amputation prosthetic prescription. The prediction model is designed to be used on patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation for chronic limb threatening ischemia (CLTI). The overall goal is to enhance prediction of future mobility to assist the rehabilitation team in setting appropriate, evidence-based goals, to better inform patient expectations, and to facilitate appropriate prosthetic prescription. Design Mixed-methods study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected patient reported mobility. Setting The Veterans Administration Corporate Data Warehouse (CDW), the National Prosthetics Patient Database (NPPD), participant mailings and phone calls. Participants 357 Veterans who underwent an incident TT or TF amputation for CLTI and received a qualifying lower limb prosthesis (LLP) between March 1, 2018, and November 30, 2020. Interventions Not applicable. Main Outcome Measures The Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6). Results Variable selection, using a machine learning methodology, led to a final model of 23 predictors that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation – levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively. Conclusions The AMPREDICT PROsthetics model can assist providers in estimating individual patient's future mobility at the time of prosthetic prescription thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for their anticipated functional goals. Author(s) Disclosures No conflicts of interest. This project was completed from funding provided by a Veterans Administration Research Rehabilitation and Development Grant. To develop and validate a patient-specific multivariable prediction model that utilizes readily available predictors to predict 12-month mobility at the time of initial post amputation prosthetic prescription. The prediction model is designed to be used on patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation for chronic limb threatening ischemia (CLTI). The overall goal is to enhance prediction of future mobility to assist the rehabilitation team in setting appropriate, evidence-based goals, to better inform patient expectations, and to facilitate appropriate prosthetic prescription. Mixed-methods study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected patient reported mobility. The Veterans Administration Corporate Data Warehouse (CDW), the National Prosthetics Patient Database (NPPD), participant mailings and phone calls. 357 Veterans who underwent an incident TT or TF amputation for CLTI and received a qualifying lower limb prosthesis (LLP) between March 1, 2018, and November 30, 2020. Not applicable. The Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6). Variable selection, using a machine learning methodology, led to a final model of 23 predictors that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation – levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively. The AMPREDICT PROsthetics model can assist providers in estimating individual patient's future mobility at the time of prosthetic prescription thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for their anticipated functional goals.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PurposeThis study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system. Methods and MaterialsA cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely. ResultsAll the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification. ConclusionThe cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Improving Radiation Quality Through the Implementation of Prospective Peer Review

<h3>Purpose/Objective(s)</h3> Quality assurance (QA) is critical to the successful delivery of radiation therapy (RT) for cancer patients and also impacts outcomes. The present study reports the longitudinal results of a prospective peer review evaluation system (PES) implemented at a major academic health system as an integral part of RT QA. <h3>Materials/Methods</h3> All patient cases treated with RT by one of 32 physicians at 14 treatment locations within our radiation oncology department undergo prospective peer review in daily rounds. Each case is assigned a grade (A, B, C) based on the following criteria: clinical suitability for RT, prescription, contours, and treatment plan, where applicable. "A" cases require no changes, "B's" require minor modification, and "C's" require major modification with re-review prior to treatment planning. The present study is a longitudinal analysis of grade trends over time by hospital, plan type, and physician. The Wilcoxon-signed-ranked test was used to compare grades during the baseline PES period (2013-2016) to the follow-up period (2017-2021). A p-value below 0.05 is considered significant. <h3>Results</h3> A total of 20,069 cases for 14,300 patients were reviewed from September 2013 to November 2021. Of the total cases, 15,659 (78%) were curative and were included in the present analysis; 5,868 from the baseline period and 9,791 in the follow-up period. The fraction of A cases decreased from 74.8% (baseline) to 64.5% (follow-up), while the fraction of B's increased from 19.4% (baseline) to 35.4% (follow-up), and the fraction of C's decreased from 5.8% (baseline) to 0.1% (follow-up). The main hospital site contributed 54.8% of all cases and 48.1% of all B grades. IMRT plans accounted for 55.4% of all plans and 68.2% of all B scores, while 3D-conformal plans accounted for 24.4% of cases and 15.4% of all B scores. When normalized by total department A/B/C scores, there were no statistically significant changes in percent A/B/C scores over the time period studied (p-value> 0.05). However, for the 13 physicians with cases throughout the study period, there was a statistically significant increase in the fraction of B scores (p value = 0.01) and decrease in the fraction of C scores (p value < 0.01) between the two time periods when individual physician grades were normalized to their individual caseload. <h3>Conclusion</h3> PES improves the identification of quality-related factors that can impact downstream radiation treatment plan quality. The diminishing fraction of C grades over time indicates amelioration of ‘major failures,' which were addressed in the startup years of PES. The proportional increase in B's, at the cost of fewer putative A's, suggests increased scrutiny of cases, as well as findings-based improvements in contouring and planning, over time. Peer review rounds upstream of treatment planning provides valuable RT QA and should be considered by other departments to enhance the quality and consistency of RT.