Reinventing the Medical Assistant Staffing Model at No Cost in a Large Medical Group.
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ABSTRACT:
SUBMITTER: Ilbawi NM
PROVIDER: S-EPMC7062490 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Publications
Reinventing the Medical Assistant Staffing Model at No Cost in a Large Medical Group.
Ilbawi Nadim M NM Kamieniarz Monica M Datta Avisek A Ewigman Bernard B
Annals of family medicine 20200301 2
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Project description:ICUs are increasingly staffed with nurse practitioners/physician assistants (NPs/PAs), but it is unclear how NPs/PAs influence quality of care. We examined the association between NP/PA staffing and in-hospital mortality for patients in the ICU.We used retrospective cohort data from the 2009 to 2010 APACHE (Acute Physiology and Chronic Health Evaluation) clinical information system and an ICU-level survey. We included patients aged ≥ 17 years admitted to one of 29 adult medical and mixed medical/surgical ICUs in 22 US hospitals. Because this survey could not assign NPs/PAs to individual patients, the primary exposure was admission to an ICU where NPs/PAs participated in patient care. The primary outcome was patient-level in-hospital mortality. We used multivariable relative risk regression to examine the effect of NPs/PAs on in-hospital mortality, accounting for differences in case mix, ICU characteristics, and clustering of patients within ICUs. We also examined this relationship in the following subgroups: patients on mechanical ventilation, patients with the highest quartile of Acute Physiology Score (> 55), and ICUs with low-intensity physician staffing and with physician trainees.Twenty-one ICUs (72.4%) reported NP/PA participation in direct patient care. Patients in ICUs with NPs/PAs had lower mean Acute Physiology Scores (42.4 vs 46.7, P < .001) and mechanical ventilation rates (38.8% vs 44.2%, P < .001) than ICUs without NPs/PAs. Unadjusted and risk-adjusted mortality was similar between groups (adjusted relative risk, 1.10; 95% CI, 0.92-1.31). This result was consistent in all examined subgroups.NPs/PAs appear to be a safe adjunct to the ICU team. The findings support NP/PA management of critically ill patients.
| S-EPMC4251618 | biostudies-other
Project description:BackgroundAs efforts continue to diversify the physician workforce so that it better matches the patient population, the number of medical students with disabilities will increase. U.S. medical schools and emergency medicine (EM) clerkships should be prepared to provide full and meaningful access to learners with disabilities.MethodsWe created a novel means of providing access to a senior medical student with a mobility disability (secondary to a cervical spinal cord injury) to participate in a fourth-year EM clerkship. We hired four second-year medical students as intermediaries to perform senior medical student-directed physical examination maneuvers, during his 15 required 8-hour emergency department shifts. The senior medical student dictated his documentation using Dragon Natural Speaking (Nuance Communications, 2015) voice recognition software.ResultsThe senior medical student successfully completed the required clinical clerkship and earned a honors grade for his work. Both the senior medical student and the second-year medical student intermediaries gave positive feedback about the experience.ConclusionsGiven the significant prevalence of disability among medical students in U.S. medical schools, medical educators should provide greater access to students with disabilities and opportunities for advanced education for all learners by creating innovative clinical curriculum. The authors recommend the student intermediary model for senior medical students with physical disabilities in required clinical clerkships.
| S-EPMC7369484 | biostudies-literature
Project description:The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.
| S-EPMC10810636 | biostudies-literature
Project description:IntroductionIn 2001, the Radiation Therapy Advisory Panel (RTAP) of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) (formerly known as Australian Institute of Radiography) published a model for radiation therapist staffing in Australian radiation oncology departments. Between 2012-2013, the model was reviewed to ensure it reflected current radiation therapy practice, technology, and to facilitate forward planning of the radiation therapy workforce.MethodTwenty-four sites from all states participated and provided data on megavoltage simulation, planning and treatment delivery. For simulation and planning activity, the length of time to complete was collected against relevant Medicare Benefits Schedule (MBS) items. For treatment delivery, time to complete activities was collected against a common set of activities. Modelling assumptions are clearly identified in the methodology.ResultsA new model was developed retaining the essential model parameter of full-time equivalent (FTE) radiation therapists (RTs) per linear accelerator operating hour as in the 2001 model but based on contemporary practice and data. The model also includes significant refinements that improve the model's overall utility and flexibility for both workforce planning purposes and for individual services to use the model according to their own organisational needs and service delivery profiles.ConclusionThe ASMIRT believes that the 2014 RT staffing model provides the utility and flexibility for radiation oncology services to best plan RT staffing establishments according to their needs and reflecting the diversity between services and within the sector. It should also provide a robust and valid basis for governments and service planners to use as a guide in workforce planning into the future.
| S-EPMC5167329 | biostudies-literature
Project description:BackgroundThe intensive care unit (ICU) staffing model affects clinical outcomes of critically ill patients. However, the benefits of a closed unit model have not been extensively compared to those of a mandatory critical care consultation model.MethodsThis retrospective before-after study included patients admitted to the medical ICU. Anthropometric data, admission reason, Acute Physiology and Chronic Health Evaluation II score, Eastern Cooperative Oncology Group grade, survival status, length of stay (LOS) in the ICU, duration of mechanical ventilator care, and occurrence of ventilator-associated pneumonia (VAP) were recorded. The staffing model of the medical ICU was changed from a mandatory critical care consultation model to a closed unit model in September 2017, and indices before and after the conversion were compared.ResultsA total of 1,526 patients were included in the analysis. The mean age was 64.5 years, and 954 (62.5%) patients were men. The mean LOS in the ICU among survivors was shorter in the closed unit model than in the mandatory critical care consultation model by multiple regression analysis (5.5 vs. 6.7 days; p = 0.005). Central venous catheter insertion (38.5% vs. 51.9%; p < 0.001) and VAP (3.5% vs. 8.6%; p < 0.001) were less frequent in the closed unit model group than in the mandatory critical care consultation model group. After adjusting for confounders, the closed unit model group had decreased ICU mortality (adjusted odds ratio 0.65; p < 0.001) and shortened LOS in the ICU compared to the mandatory critical care consultation model group.ConclusionThe closed unit model was superior to the mandatory critical care consultation model in terms of ICU mortality and LOS among ICU survivors.
| S-EPMC8550369 | biostudies-literature
Project description:ObjectiveTo estimate the relationship between outcomes of care and medical practices' structure and use of organized care improvement processes.Data sources/study settingWe linked Medicare claims data to our national survey of physician practices (2012-2013). Fifty percent response rate; 1,040 responding practices; 31,888 physicians; 868,213 attributed Medicare beneficiaries.Study designCross-sectional observational analysis of the relationship between practice characteristics and total spending, readmissions, and ambulatory care-sensitive admissions (ACSAs), for all beneficiaries and five categories of beneficiary defined by predicted need for care.Principal findingsPractices with 100+ physicians and 50-99 physicians had, respectively, annual spending per high-need beneficiary that was $1,870 (12.5 percent) and $1,824 higher than practices with 1-2 physicians, and readmission rates 1.64 and 1.71 higher. ACSA rates did not vary significantly by practice size. Outcomes did not vary significantly by ownership or by practices' use of organized processes to improve care.ConclusionsLarge practices had higher spending and readmission rates than the smallest practices, especially for high-need beneficiaries. There were no significant performance differences between physician-owned and hospital-owned practices. Policy makers should consider the effects of specific policies on provider organization, pending further research to learn which types of practice provide better care.
| S-EPMC6232442 | biostudies-literature
Project description:ObjectivePhysician assistant (PA) and nurse practitioner (NP) staffing is increasingly common in emergency departments (EDs), with variable physician supervision. We examined the feasibility of using publicly reported metrics as a measure of ED performance by staffing model.MethodsWe classified a convenience sample of 915 EDs by staffing model using the National Emergency Department Inventory-USA 2016 and a follow-up survey. Staffing models included 24/7 attending coverage with PAs/NPs, 24/7 attending coverage without PAs/NPs, and PAs/NPs without 24/7 attending coverage. We linked EDs with Hospital Compare data to examine availability of metrics and compared metric performance by staffing model. We used regression modeling to examine the independent relationship between staffing model and ED performance after adjusting for ED characteristics.ResultsOf 915 EDs surveyed, 767 (83%) responded and 436 (48%) had complete staffing data and any Hospital Compare data. The 216 EDs without any Hospital Compare data more frequently had no 24/7 attending coverage, were smaller, and were more often rural. Of 5 clinical metrics, 3 had data from < 100 EDs (range: 2%-21%), and 2 had data from 0 EDs. Of the 5 clinical metrics, only median time-to-ECG had enough data for analysis and found no difference among staffing models. Among the 3 process metrics, only time to discharge was significantly faster in EDs with any PA/NP staffing.ConclusionMany EDs in our national sample lacked sufficient Hospital Compare data to evaluate performance, likely because of lower patient volumes for condition-specific metrics. Alternative strategies to measure quality of care delivery in these settings should be developed.
| S-EPMC8760948 | biostudies-literature
Project description:BackgroundExcessive inpatient administrative tasks can lead to adverse consequences for residents and their patients. Furthermore, this burden has been linked to depersonalization, a major component of physician burnout.ObjectiveTo describe the development, implementation, feasibility, acceptability, and early outcomes of Resident Team Assistant (RTA) programs.MethodsThree large academic medical centers created RTA programs in which administrative assistants are incorporated into inpatient medical teams. First steps included a needs assessment and driver diagram creation to identify key issues and to solidify goals. Program directors were assigned, and RTAs were hired, trained, and incorporated into inpatient teams at each institution (2003, 2016, 2018). Program leadership and institutional stakeholders met regularly to discuss development and quality assurance. Surveys and direct interviews were performed to evaluate impact and acceptability. Institutional goals in accordance to RTAs tasks were also investigated.ResultsResident surveys and interviews have shown acceptability with RTAs completing a large percentage of resident administrative tasks while promoting time spent in direct clinical care and job satisfaction. Hospital-specific improvements have included increase in referring physician communication rate and decrease in work hour violations. The programs have maintained high feasibility and sustainability with a relatively low time commitment from leadership and cost for the institutions.ConclusionsThe RTA programs at the 3 institutions have continued to be sustained over time with perceived improvements in administrative task burden and job satisfaction for the residents. They have maintained high acceptability and feasibility in terms of effort and costs for the hospitals.
| S-EPMC7771612 | biostudies-literature
Project description:ObjectivesTo determine the impact of unit-level nurse staffing on quality of discharge teaching, patient perception of discharge readiness, and postdischarge readmission and emergency department (ED) visits, and cost-benefit of adjustments to unit nurse staffing.Data sourcesPatient questionnaires, electronic medical records, and administrative data for 1,892 medical-surgical patients from 16 nursing units within four acute care hospitals between January and July 2008.DesignNested panel data with hospital and unit-level fixed effects and patient and unit-level control variables.Data collection/extractionRegistered nurse (RN) staffing was recorded monthly in hours-per-patient-day. Patient questionnaires were completed before discharge. Thirty-day readmission and ED use with reimbursement data were obtained by cross-hospital electronic searches.Principal findingsHigher RN nonovertime staffing decreased odds of readmission (OR=0.56); higher RN overtime staffing increased odds of ED visit (OR=1.70). RN nonovertime staffing reduced ED visits indirectly, via a sequential path through discharge teaching quality and discharge readiness. Cost analysis projected total savings from 1 SD increase in RN nonovertime staffing and decrease in RN overtime of U.S.$11.64 million and U.S.$544,000 annually for the 16 study units.ConclusionsPostdischarge utilization costs could potentially be reduced by investment in nursing care hours to better prepare patients before hospital discharge.
| S-EPMC3207188 | biostudies-literature
Project description:Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.
| S-EPMC10693607 | biostudies-literature