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Commentaries on health services research

2017· article· en· W2907177766 on OpenAlexaboutno aff
Roderick S. Hooker, James F. Cawley, Guillermo V. Sanchez, Davis G. Patterson, Pauline Joyce

Bibliographic record

VenueJAAPA · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAmbulatoryMedicineWorking timeWork (physics)Work hoursHealth careWorking hoursWork timeMotion (physics)Ambulatory careFamily medicineSurgeryComputer scienceEngineering

Abstract

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Clinical time allotted to patients and charting in ambulatory care ABSTRACT The authors used a time and motion study (office hours) and self-reported diary (after hours) to describe how time is spent in ambulatory practice. Family medicine, internal medicine, cardiology, and orthopedic physicians were observed. In-office physicians spent 27% of their total time on direct clinical face time with patients and 49% of their time on electronic health records (EHRs) and deskwork. While in the examination room with patients, they spent 53% of the time on direct clinical face time and 37% on EHRs and deskwork. Physicians reported 1 to 2 hours of after-hours work each night, devoted mostly to EHR tasks. For every hour physicians provide direct clinical face time to patients, nearly 2 additional hours is spent on EHRs and deskwork in the clinic day. Outside office hours, physicians spend another 1 to 2 hours of personal time each night doing additional computer and other clerical work.1 Commentary by Roderick S. Hooker: A time and motion study is a business efficiency technique combining the time study work of Frederick Taylor with the motion study work of Frank and Lillian Gilbreth a century ago. This technique remains applicable today in human resource and animal observation investigations.2 Additionally, time-motion data are typically considered highly valued evidence in efficiency research. Jane Record's seminal time-motion study on PAs and supervising physicians in the 1970s remains embedded in medical labor economics as early evidence of team-based synergy.3 What this American Medical Association-sponsored physician time-motion study illustrates is that EHRs are culprits in offsetting patient time with clerical work that consumes twice as much labor. EHRs were not present in early organizational studies of physicians and other health professionals but have now emerged to be the bane of existence for many fatigued providers. This outpatient study validates inpatient observations that what providers perceive they are doing daily differs significantly from what they are systematically observed to be doing.4 REFERENCES Rethinking the primary care workforce ABSTRACT Adults in the United States will soon have a different primary care experience than we have been used to. In the primary care practice of the future, the physician's role will increasingly be played by NPs. In addition, the 150 million adults with one or more chronic conditions will receive some of their care from RNs functioning as care managers. Workforce experts agree on the growing gap between the population's demand for primary care and the number of primary care physicians available to meet that demand. The number of NPs entering the workforce each year has mushroomed from 6,600 in 2003 to 18,000 in 2014. The number of primary care NPs is projected to increase 84% between 2010 and 2025. The number of PAs entering the workforce also is growing, though not as rapidly. More and more patients will see an NP or a PA as their primary care provider. Physicians probably will focus on diagnostic conundrums and lead teams caring for patients with complex healthcare needs. Although the NP role begins to approximate that of the physician, RNs are assuming important emerging primary care functions, such as managing the care of patients with chronic disease.1 Commentary by James F. Cawley: The authors of this editorial, an academic primary care physician and a nurse, argue for a greater reliance on NPs for the delivery of primary care and propose an expanded role for RNs as care managers for patients with chronic disease. They note the substantial projected shortage of primary care physicians and the expanding numbers of annual NP graduates and suggest that the shortage of primary care providers (physicians, NPs, and PAs) to population is likely to decline. Given these trends, plus the fact that only 50% of NPs and 32% of PAs work in primary care specialties, the proposal for an expanded role for RNs in the case manager role seems logical. Whether a substantial number of RNs will seek such roles remains a question. With the increasing prevalence of chronic disease, new workforce strategies are needed, such as those proposed in this editorial as well as others including additional incentives for PAs and NPs to select primary care positions. REFERENCE Who is more likely to prescribe antibiotics for a URI? ABSTRACT Differences in antibiotic prescribing rates for pediatric upper respiratory infections (URIs) between physicians and NPs were abstracted from the National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS). URIs accounted for about 439 ± 21.5 million visits. Patients seen by NPs were more likely to have Medicaid, live in the lowest median household income quartile ZIP codes and micropolitan locations, and live in the South compared with patients seen by physicians. NPs prescribed antibiotics 66.7% ± 4.2% of the time versus physicians at 52.8% ± 0.8% for URI visits. Adjusted by specialty, URI type, and chronic diseases, NPs had marginally significantly different odds of prescribing antibiotics. Patient visits to a pediatric or ear-nose-throat/surgery practice had lower odds of antibiotic prescribing compared with family medicine practices. Year (2001-2010) was not significantly associated with antibiotic or broad-spectrum antibiotic prescribing rates for physicians, but rates for NPs fell for otitis media from 90.2% ± 8.2% to 74.8% ± 6.8% of visits. Because NPs have higher rates of antibiotic prescribing compared with physicians for children with URIs, examining comparative antibiotic prescribing is important to promote evidence-based practice and adoption of clinical guidelines.1 Commentary by Guillermo V. Sanchez: Ference and colleagues reported that NPs have higher rates of antibiotic prescribing compared with physicians for pediatric respiratory visits, suggesting that NPs may be more likely to prescribe inappropriately. The authors used NAMCS and NHAMCS data. However, NAMCS was not designed to capture NP prescribing because patient encounters are sampled from physicians rather than institutions.2 Visits to NPs are captured in NAMCS, but NP visits are undersampled.2 Although other studies have suggested NP prescribing is higher compared with physicians, these studies are not representative.3-5 Further studies describing NP and PA antibiotic prescribing are needed. However, regardless of profession, available evidence clearly indicates that outpatient antibiotic prescribing is much higher than desired or necessary. Initiatives to improve outpatient antibiotic use should target NPs in addition to physicians and PAs.6 REFERENCES How satisfying is rural practice among three types of clinicians? ABSTRACT This study investigated factors associated with dissatisfaction or satisfaction with rural practice among physicians, PAs, and NPs employed by an integrated healthcare delivery network in rural New York State. Administrative data about practice units were linked with cross-sectional data from a self-administered multidimensional questionnaire that contained practitioner demographics plus valid scales assessing autonomy/relatedness needs, risk aversion, tolerance for uncertainty/ambiguity, meaningfulness of patient care, and workload. Factors with significant fixed effects were entered into regression models of the proportion of time that practitioners were dissatisfied or satisfied. Of the 473 eligible participants, 59.1% of respondents were doctoral-level and 40.9% PAs and NPs. Clinicians with heavier workloads and/or who had less tolerance for uncertainty were less likely to be highly satisfied with their work; those deriving greater meaning from practice were more likely. Practitioners who found their work meaningful and had good relationships with colleagues were less likely to be dissatisfied. Practitioner demographics and most practice unit characteristics did not have any independent effect. Mutable factors, such as workload, work meaningfulness, relational needs, uncertainty/ambiguity tolerance, and risk-taking attitudes had the strongest association with practitioner satisfaction or dissatisfaction, independent of demographics and practice unit characteristics. Organizational efforts should be dedicated to a redesign of group-employment models, including more equitable division of clinical labor, building supportive peer networks, and uncertainty/risk tolerance coaching, to improve the quality of work life among rural practitioners.1 Commentary by Davis G. Patterson: The notion of the “Quadruple Aim” adds a much-needed fourth dimension—practitioner health and satisfaction—to the Triple Aim of health reform that focuses on the patient and the system but forgets the provider.2 The backdrop to this study of provider satisfaction and dissatisfaction is the changing rural healthcare landscape in an age of organizational consolidation, which is also changing the very meaning of being a rural practitioner. Is this consolidation in pursuit of the Triple Aim a threat to achieving the Quadruple Aim? Not necessarily. The good news from this study is that the authors found most of the factors that predicted work satisfaction (and dissatisfaction) in a rural group model undergoing change were potentially modifiable by employers. Organizational affiliation can present an opportunity to stabilize fragile rural practices and offer a source of support for isolated providers, but only if employers pay critical attention to the work life and needs of the care team, and educational programs train new professionals to have the “right stuff” for this new environment. REFERENCES Canadian-based case studies of PA models of care ABSTRACT Delivering high-quality, effective, and sustainable services is both a top priority and one of the most pressing challenges facing governments and businesses as they look to balance healthcare demands and costs in the context of an aging population. PAs are a relatively new profession in Canada. One of the challenges of the profession in Canada is the lack of data on the effect of PAs from a productivity and cost-effectiveness perspective. This report aims to set the stage and provide context to better understand the role of PAs in various health settings across Canada.1 Commentary by Pauline Joyce: Although studies have demonstrated the multiple benefits that PAs offer to health systems, their acceptance in Canada remains low. The authors of this case-based study suggest the need for removal of funding and remuneration barriers before PAs can optimize their contributions to Canada's healthcare systems. As one would expect, the lack of regulation is a significant barrier, as other provider groups see it as a patient safety issue. The key arguments in this report center on the need for more retrospective and prospective case-control research studies to measure the quantitative effect of PAs in building a better business case. Several projects in the report demonstrate that PAs can deliver similar, or even better, outputs or outcomes for designated competencies with respect to other health professionals. Communicating the results of pilot project successes and ways to overcome challenges should be shared with stakeholders across the country, especially on leading practices. REFERENCE

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.253
GPT teacher head0.427
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2017
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