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Record W2765557097 · doi:10.5334/ijic.3424

Advanced Practice Physiotherapy Progressing Integrated Care of Musculoskeletal Disorders in Ireland: a National Evaluation

2017· article· en· W2765557097 on OpenAlexaffabout
Orna Fennelly, Catherine Blake, Oliver FitzGerald, Roisin Breen, Jennifer Ashton, Aisling Brennan, Caitríona Cunningham

Bibliographic record

VenueInternational Journal of Integrated Care · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsMedicineReferralPhysical therapyDescriptive statisticsFamily medicine

Abstract

fetched live from OpenAlex

Introduction: Musculoskeletal (MSK) disorders are a major burden on patients, families and healthcare systems(1). Similar to international initiatives(2, 3), Advanced Practice Physiotherapist (APP) posts (n=22) were introduced to optimize patient flow between primary and acute hospital MSK services at 16 acute hospitals across Ireland in 2012. APP services provide an alternative access route to hospital rheumatology and orthopaedic services (Figure 1) with their scope of practice including traditionally medically-controlled acts: administering injections, ordering investigations, triaging for onward referral to hospital specialities and surgical listing. This study analysed the national MSK data collected by APPs over 2014, using descriptive statistics. The objectives of this study are: to profile the national APP patient caseload, evaluate efficiency and establish clinical outcomes of APP appointments.Results: APPs assessed 13,981 new patients, with 2,596 return patients reviewed. Knee (23%), lower back (22%) and shoulder (15%) disorders were most prevalent. Patients waited a median time of 167 days (IQR 91-316) for their appointment, with favourable reductions noted for patients referred during 2014 (n=6,552) with mean wait times of 107±64 days. For 79% (n=10,036) of new patients, consultant input on patient care management was not required at the APP assessment, thus freeing up consultant time for more urgent cases. The clinical decision made by the APP shows the relatively low proportions of resources required and the demand for conservative management:Primary Care:Physiotherapy: 22% (n=3,568)Secondary Care:Physiotherapy: 19% (n=3,130)MSK injection: 4% (n=685)Clinical Investigations: 29% (=4,833)Clinical Imaging (5-month period): 27% (n=7,010)Orthopaedic/Rheumatology services: 18% (n=2,818)Other Hospital Speciality: 4% (n=563)Surgical Intervention: 2% (n=404)Discussion: The first national evaluation of MSK APP services has demonstrated that as part of an integrated care pathway, APPs can manage a multiplicity of disorders. The addition of APP services enhanced benefits to patients with faster access to specialist MSK care; optimizing flow of patients between primary and secondary care. Screening of referral letters selected appropriate patients for APP assessments with few requiring onward referrals to orthopaedic/rheumatology services. In addition to this, the greater demands for conservative management, high independent APP management and low resource utilisation indicated that APPs may be a more appropriate access route for many MSK patients.Conclusion: The assimilation of APP and consultant MSK services, represents a fast, resource efficient addition to the integrated care pathway for MSK disorders. The continued collection of national service data will enable ongoing service evaluation and development through monitoring key performance indicators.Lessons learned: Collection of national databases enables comprehensive service evaluations.Introduction of APPs reduced waiting times for all patients enabling faster access routes between primary care and specialist MSK care.Given the proportion of patients deemed not to require specialist MSK management, greater resourcing of primary care services could remove necessity for referral to the acute hospital system.Limitations: The current fields in the national database do not track the patient journey throughout the Integrated Care Pathway with data limited to the acute setting only.Future: Future research should gain patients' perspective and evaluate patient-centred outcomes.References:1- Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bulletin of the World Health Organization. 2003;81(9):646-56.2- Kennedy DM, Robarts S, Woodhouse L. Patients are satisfied with advanced practice physiotherapists in a role traditionally performed by orthopaedic surgeons. Physiotherapy Canada. 2010;62(4):298-305 8p.3- Rabey M, Morgans S, Barrett C. Orthopaedic physiotherapy practitioners. Clinical Governance: An International Journal. 2009;14(1):15-9

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.495
Teacher spread0.469 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations1
Published2017
Admission routes2
Has abstractyes

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