Revised service metrics partially explain variation in outcomes across facilities in a state‐wide advanced practice public musculoskeletal service
2022; Wiley; Volume: 21; Issue: 2 Linguagem: Inglês
10.1002/msc.1717
ISSN1557-0681
AutoresMaree Raymer, Peter Window, Michelle Cottrell, Tracy Comans, Shaun O’Leary,
Tópico(s)Global Health Workforce Issues
ResumoMusculoskeletal CareEarly View SHORT REPORTOpen Access Revised service metrics partially explain variation in outcomes across facilities in a state-wide advanced practice public musculoskeletal service Maree Raymer, Corresponding Author Maree Raymer [email protected] orcid.org/0000-0002-0833-2160 Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia Correspondence Maree Raymer, Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia. Email: [email protected]Search for more papers by this authorPeter Window, Peter Window Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, AustraliaSearch for more papers by this authorMichelle Cottrell, Michelle Cottrell Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, AustraliaSearch for more papers by this authorTracy Comans, Tracy Comans Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston, Queensland, AustraliaSearch for more papers by this authorShaun O'Leary, Shaun O'Leary orcid.org/0000-0002-2574-129X Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, Queensland, AustraliaSearch for more papers by this author Maree Raymer, Corresponding Author Maree Raymer [email protected] orcid.org/0000-0002-0833-2160 Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia Correspondence Maree Raymer, Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia. Email: [email protected]Search for more papers by this authorPeter Window, Peter Window Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, AustraliaSearch for more papers by this authorMichelle Cottrell, Michelle Cottrell Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, AustraliaSearch for more papers by this authorTracy Comans, Tracy Comans Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston, Queensland, AustraliaSearch for more papers by this authorShaun O'Leary, Shaun O'Leary orcid.org/0000-0002-2574-129X Royal Brisbane and Women's Hospital, Department of Physiotherapy, Metro North Hospital Health Service, Brisbane, Queensland, Australia School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, Queensland, AustraliaSearch for more papers by this author First published: 27 November 2022 https://doi.org/10.1002/msc.1717AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 1 INTRODUCTION Health services that function across multiple facilities inevitably experience variation in service characteristics between facilities to accommodate local health settings, nuances and priorities (Partington et al., 2017). In this situation it is recommended that potential sources of variation between facilities are identified to better inform future serve planning, ensuring equitable and efficient provision of care (ACSQHC, 2013; Partington et al., 2017). This can be challenging in musculoskeletal care as systems and processes for meaningful benchmarking of musculoskeletal services are in their infancy (Burgess et al., 2022) One example of a musculoskeletal service recently reported to have variation in outcomes across its numerous facilities is the Neurosurgical and Orthopaedic Physiotherapy Screening Clinic and Multi-disciplinary Service (N/OPSC&MDS) (Raymer et al., 2021). The N/OPSC&MDS is an advanced practice physiotherapist-led model of care designed to address overburdened public orthopaedic and neurosurgical medical specialist outpatient services across Queensland, Australia (Moretto et al., 2019). Selected patients with musculoskeletal conditions waitlisted on medical specialist outpatient services are initially assessed by an Advanced Musculoskeletal Physiotherapist. If assessed as potentially amenable to non-surgical management, patients are usually referred for a trial of pragmatic multidisciplinary care (e.g. physiotherapy, psychology, dietetics, occupational therapy, pharmacy, as required) (Cottrell et al., 2018). While operational in 18 facilities (17 hospitals and one community-based facility) under common principles, the services vary in scale and are tailored to the local context accommodating the local patient case mix, as well as health service and organisational priorities and processes. In a previous audit (between 2012 and 2017) of the N/OPSC&MDS, benefits to overburdened specialist outpatient services were evident in that nearly 70% of patients managed within the service were discharged without requiring specialist medical consultation (primary service outcome of the N/OPSC&MDS) (Raymer et al., 2021). This figure aligned with 69% of patients in the audit reporting clinically meaningful improvements in their condition (Global Rating of Change [GROC], Primary Clinical Outcome). However, substantial variation in the primary service outcome was observed between N/OPSC&MDS facilities statewide, with 15/18 facilities significantly different to the referent facility. This was despite the primary clinical outcome only varying significantly at 3/18 facilities. Crucially, adjustment for service-related variables inflated variation in the primary service outcome from 10 to 15 facilities with remaining uncertainty regarding other potential sources of facility variation (only 32% explained variance) (Raymer et al., 2021). Given that service planning and quality improvement depends on identifying service-related variables potentially impacting outcomes, a revised set of N/OPSC&MDS standardised state-wide metrics were implemented following the audit. These were based on reviews of relevant epidemiological (Fehring, 2016; Sangha et al., 2003; Yen et al., 2015), patient related outcome measures (Fennelly et al., 2018; Hill et al., 2020; Nicholas et al., 2015), and chronic disease database (national and international) literature (Clement et al., 2015; ICHOM, 2017; Williams et al., 2016). This short report evaluates the impact of implementing the revised state-wide metrics in better explaining outcome disparities between N/OPSC&MDS facilities, particularly the primary service outcome of discharge pathway. It is hypothesised the previously observed variation in primary service outcome of this multi-facility musculoskeletal service will be better explained by the refined service metrics. Alternatively, if variation in the primary service outcome is not better explained with refined service metrics, variation may instead reflect differences in service provision between facilities with implications for future service planning and quality improvement. 2 METHODS Adopting the same methods as the previous study (Raymer et al., 2021), the N/OPSC&MDS Measurement Analysis and Reporting System database was audited in a more recent period (1st July 2018 to 30th June 2020) following implementation of the revised metrics at 18 eligible service facilities. As previous, the primary service outcome was dichotomised as either Discharged (discharged from the service with no specialist medical review required) or Specialist RV (reinstated for specialist medical review), and the primary clinical outcome dichotomised as either Responder (achieved clinically meaningful change; +2 to +5 score) or Non-Responder (not achieving clinically meaningful change; −5 to +1 score) based on the 11-point GROC scale (Kamper et al., 2009; Raymer et al., 2021). Table 1 demonstrates the revised N/OPSC&MDS state-wide metrics that incorporate secondary outcome measures and explanatory variables potentially contributing to variation. TABLE 1. Secondary outcomes and potential explanatory variables (units/categories) included in the analysis Secondary service—Related outcomes and explanatory variables Outpatient service (orthopaedic, neurosurgical, spinal pathway, direct GP)—Indicates source of the original referral. Triage category (1–3)—Patient referrals are categorised as urgent (category 1), semi-urgent (category 2) or non-urgent (category 3) with recommended timeframes for an initial outpatient consultation within 30, 90, and 365 days, respectively. Waiting time (days)—Time between specialist outpatient department receipt of initial referral and initial N/OPSC & MDS appointment. Management duration (days)—Time between initial N/OPSC&MDS appointment and discharge Review appointments (absolute number)—Number of patients receiving an N/OPSC&MDS review appointment. Non-attendance (yes/no)—Number of patients not attending the final N/OPSC&MDS review appointment. Multidisciplinary referrals (number)—Number of patients referred to multidisciplinary treatment services (may be one or more of the services as clinically indicated). Medical specialist case discussion (yes/no)—Number of patients for whom case discussion with a medical consultant was sought during the N/OPSC&MDS management period. Investigations initiated (yes/no)—Number of patients for whom any investigations were initiated as part of N/OPSC&MDS management. Interventions initiated (yes/no)—Number of patients for whom any interventions (injection/s, prescribing) were initiated as part of N/OPSC&MDS management. Secondary patient—Related outcomes and explanatory variables Sociodemographic measures Age (years) Gender (male/female/indeterminate) Country of birth (born in Australia (yes/no) Aboriginal and Torres Strait Islander status (not Aboriginal or Torres Strait Islander, Aboriginal and/or Torres Strait Islander) Employment status (employed, unemployed, retired, other) Level of education (up to lower secondary, secondary, post school qualifications) Smoking status (current smoker, former smoker, never smoked) Socioeconomic indexes for areas (SEIFA) of advantage/disadvantage - score based on residential postcode with 85% of service-related variables, >70% of the patient socio-demographic measures), with minimal variation in data completeness across facilities. There was a lower completion rate for the primary (GROC, 54% of those eligible completed) and secondary (completion rate range 52%–75% at baseline, 20%–57% at discharge) clinical outcome measures. Approximately 54% of patients were either discharged at their initial N/OPSC & MDS assessment or did not require review by the Service Leader and therefore were not eligible to complete discharge clinical outcomes. Across all facilities 72.5% of discharged patients did not require a medical specialist review (Primary service outcome), although this outcome varied across conditions as shown in Table 2. Across all facilities 67.1% of patients reported a clinically meaningful response to management within the N/OPSC&MDS (Supplementary Table 1). TABLE 2. Service outcomes and explanatory variables presented as the number (n) and proportion (%) of patients within each category Variables n % Primary service outcome—discharge pathway Discharged 13,850 72.5% Cervical/Thoracic/Lumbar/Sacro-iliac 6420 79.2% Shoulder/Elbow/Wrist/Hand 2361 72.8% Hip/Knee/Ankle/Foot 4936 65% Specialist review 5256 27.5% Potential explanatory variables Outpatient service Orthopaedic 13,129 68.7% Neurosurgical 2361 12.4% Spinal pathway 2926 15.3% Direct GP 528 2.8% Other 4 0.0% Triage category 1 131 0.7% 2 5480 28.7% 3 13,495 70.6% Waiting time <2 weeks 207 1.1% 3 months 10,470 54.8% Management duration 1 Day 4950 25.9% 3 months 8061 42.2% Review appointments (yes) 7412 46.2% Non-attendance 1831 9.6% Multidisciplinary referrals (yes) 10,966 57.4% Medical specialist case discussion during N/OPSC and MDS management 3387 17.7% Initiation of interventions (yes) 579 3.6% Initiation of investigations (yes) 1779 11.1% Note: Service data completion rates were high; > 85% of service-related variables with minimal variation in data completeness across facilities. Hierarchical binomial regression modelling findings for the primary service (Discharged) and clinical (Responder) outcomes are shown in Table 3 and Supplementary Table 2, respectively. Preliminary analyses demonstrated many of the patient-related variables (Pain Severity, STarT MSK, Pain Self Efficacy Questionnaire—Short Form (PSEQ-2), Oswestry Disability Index (ODI), Neck Disability Index (NDI), Quick Disabilities of Arm, Shoulder and Hand (QDASH), Lower Extremity Functional Scale (LEFS)) to be significantly correlated (Spearman's rho 0.43–0.72, p < 0.001). To avoid multi-collinearity in the multivariable model, only the Pain Severity, STarT MSK and AQOL-4D variables were selected to be carried through to the multivariate analysis based on their relevance to all conditions (investigator's judgement). The Outpatient Service (e.g. neurosurgical, orthopaedic) and Condition Managed variables were also significantly related (Spearman's rho 0.88, p < 0.001), as were Management Duration and number of Review Appointments variables (Spearman's rho 0.55, p < 0.001). Therefore only the Condition Managed and Management Duration variables, respectively, were included in the multivariate analysis. The Box-Tidwell procedure (Box & Tidwell, 1962) was performed using the variables remaining in the final models and the logit of the dependent variables (Pathway outcome, Clinical outcome). All continuous variables (BMI, Pain severity, AQOL, SEIFA score) demonstrated linearity (p > 0.05) with the logit of both dependent variables. TABLE 3. Three hierarchical binomial regression models for the primary service outcome (Discharged) evaluating the relationship between discharge pathway and N/OPSC & MDS facility only (Model 1), adjusted for the patient-related variables (Model 2), adjusted for service-related variables (Model 3) Model 1: Facility only Model 2: Facility and patient variables Model 3: Facility, patient and service variables Variable Interpretation OR (95% CI) Sig. OR (95% CI) Sig. OR (95% CI) Sig. Service facility 1 Referent Referent Referent 2 0.73 (0.60–0.89) 0.00 1.30 (0.85–1.99) 0.22 0.60 (0.36–1.00) 0.05 3 0.85 (0.72–1.00) 0.04 1.21 (0.84–1.73) 0.31 0.67 (0.43–1.03) 0.07 4 2.26 (1.69–3.01) <0.001 3.38 (1.09–10.49) 0.04 5.69 (1.50–3.03) 0.01 5 0.38 (0.31–0.48) <0.001 0.34 (0.02–6.25) 0.47 3.84 (0.23–0.03) 0.35 6 2.42 (2.01–2.90) <0.001 3.13 (2.11–4.63) <0.001 1.74 (1.07–2.03) 0.03 7 1.55 (1.19–2.02) 0.00 2.60 (1.62–4.18) <0.001 1.99 (1.08–2.03) 0.03 8 2.19 (1.72–2.77) <0.001 3.99 (2.50–6.38) <0.001 6.49 (3.53–2.03) <0.001 9 1.26 (0.96–1.67) 0.10 1.63 (1.00–2.67) 0.05 1.96 (1.04–1.03) 0.04 10 1.68 (1.43–1.96) <0.001 1.77 (1.21–2.58) 0.00 4.82 (2.70–1.03) <0.001 11 0.51 (0.43–0.60) <0.001 0.94 (0.66–1.34) 0.73 1.95 (1.24–0.03) 0.00 12 0.96 (0.82–1.12) 0.57 1.06 (0.71–1.58) 0.78 1.91 (1.07–1.03) 0.03 13 2.79 (2.09–3.73) <0.001 3.96 (2.50–6.27) <0.001 5.17 (2.85–3.03) <0.001 14 1.74 (1.34–2.25) <0.001 2.15 (1.25–3.70) 0.01 0.87 (0.48–2.03) 0.65 15 0.23 (0.19–0.29) <0.001 0.64 (0.43–0.94) 0.02 0.36 (0.20–0.03) <0.001 16 0.97 (0.75–1.25) 0.81 1.24 (0.75–2.07) 0.40 1.13 (0.58–1.03) 0.72 17 1.42 (1.17–1.74) <0.001 1.98 (1.13–3.48) 0.02 0.84 (0.42–1.03) 0.61 18 0.75 (0.63–0.88) <0.001 1.24 (0.85–1.79) 0.27 0.66 (0.42–0.03) 0.07 Patient-related variables Age (years) 0–19 Referent Referent 20–39 0.72 (0.39–1.33) 0.30 0.70 (0.34–1.44) 0.33 40–59 0.83 (0.45–1.52) 0.53 0.79 (0.39–1.61) 0.52 60–79 0.65 (0.35–1.21) 0.18 0.68 (0.33–1.39) 0.29 80+ 0.56 (0.28–1.12) 0.10 0.57 (0.25–1.29) 0.18 Sex Female 1.40 (1.24–1.58) <0.001 1.28 (1.11–1.48) <0.001 Body Mass index 1.01 (1.00–1.02) 0.01 1.00 (0.99–1.01) 0.84 Pain severity 0.87 (0.84–0.90) <0.001 0.87 (0.83–0.91) <0.001 Quality of life 1.02 (1.01–1.02) <0.001 1.02 (1.01–1.02) <0.001 SEIFA score 1.00 (1.00–1.00) 0.18 1.00 (1.00–1.00) 0.66 Condition managed Spinal Referent Referent Upper limb 0.68 (0.52–0.88) 0.00 0.44 (0.33–0.60) <0.001 Lower limb 0.48 (0.38–0.60) <0.001 0.31 (0.24–0.41) <0.001 STarT MSK category Low risk Referent Referent Medium risk 0.68 (0.53–0.86) 0.00 0.60 (0.45–0.79) <0.001 High risk 0.55 (0.42–0.74) <0.001 0.50 (0.36–0.70) <0.001 Country of birth Overseas 0.86 (0.75–0.98) 0.03 0.92 (0.78–0.08) 0.31 Employment status Employed Referent Referent Unemployed 1.17 (0.99–1.38) 0.06 1.06 (0.87–1.29) 0.55 Retired 0.89 (0.73–1.08) 0.23 0.88 (0.70–1.10) 0.27 Other 0.78 (0.63–0.97) 0.02 0.82 (0.63–0.06) 0.13 Level of education Up to lower secondary Referent Referent Upper secondary 0.98 (0.83–1.16) 0.81 0.97 (0.79–1.18) 0.73 Post-school qualifications 0.98 (0.85–1.12) 0.72 1.12 (0.95–1.32) 0.18 Smoking status Current smoker Referent Referent Never smoked 1.04 (0.88–1.23) 0.65 1.00 (0.81–1.23) 0.98 Ex-smoker 1.00 (0.85–1.18) 0.99 0.93 (0.76–1.15) 0.51 Non-prescription analgesia No 0.99 (0.86–1.14) 0.89 0.94 (0.79–1.11) 0.43 Opioid analgesia No 1.05 (0.93–1.20) 0.43 1.05 (0.90–1.22) 0.57 Number of Co-morbidities None Referent Referent 1–2 0.95 (0.80–1.13) 0.58 0.96 (0.78–1.18) 0.69 3–4 1.00 (0.81–1.24) 0.98 1.16 (0.90–1.50) 0.25 5+ 1.32 (0.97–1.79) 0.08 1.19 (0.83–1.71) 0.34 Aboriginal and/or Torres Strait Islander Yes 1.24 (0.90–1.71) 0.18 1.08 (0.73–1.60) 0.70 Service-related variables Waiting time <2 weeks Referent 3 months 0.85 (0.43–1.68) 0.64 Treatment period 0 Referent 3 months 1.79 (1.48–2.17) <0.001 Triage Routine (category 3) Referent Urgent (category 1) 0.44 (0.15–1.27) 0.13 Semi-urgent (category 2) 0.66 (0.46–0.93) 0.02 Attendance at final review No 5.88 (3.92–8.80) <0.001 Medical consult during management Yes 0.05 (0.04–0.06) <0.001 Multidisciplinary team referral Yes 1.63 (1.36–1.94) <0.001 Investigations initiated Yes 0.67 (0.52–0.85) 0.00 Intervention initiated Yes 0.73 (0.50–1.07) 0.11 Interpreter required Yes 1.58 (0.91–2.74) 0.10 Note: Significant service variables in the final model included; management duration, triage category, non-attendance, medical specialist input, multidisciplinary team referral, and initiation of investigations. Shaded cells represent sites with significant variance from referent facility (Facility 1). Discharge pathway (primary service outcome) The three progressive hierarchical binomial regression models for the primary service outcome of Discharge Pathway (reference: returned to specialist outpatients waitlist) are shown in Table 3 (Clinic 1 was coded as the Referent). In Model 1, 15 facilities are seen to be significantly different to the Referent, reducing to 9 facilities in Model 2 (adjusted for patient-related variables), and increasing to 10 facilities in Model 3 (adjusted for both patient- and service-related variables). Significant service variables in the final model included; Management Duration, Triage Category, Non-Attendance to final review, and Medical Specialist Input during N/OPSC management. No outliers were evident for the primary service outcome based on the studentised residual range (SD) (−3.04–3.0 [2.09]) being within accepted parameters (≤−5, ≥5) based on 26 predictor variables in the final models (Gray & Woodall, 1994). The logistic regression model was statistically significant, χ2 (60) = 2730, p < 0.001. The model explained 49.7% (Nagelkerke R2) of the variance in pathway outcome and correctly classified 82.2% of cases. GROC (primary clinical outcome) The three progressive models of the hierarchical binomial regression for the primary clinical outcome of GROC (reference: non-response to management) is shown in Supplementary Table 2 (Clinic 16 was coded as the Referent). In Model 1, 6 facilities were significantly different to the Referent, reducing to four facilities in Model 2 (adjusted for the patient-related variables), and reducing to three facilities in Model 3 (adjusting for both patient- and service-related variables). Significant service variables in the final model included; Medical Specialist Input. No outliers were evident for the primary clinical outcome according to the studentised residuals (range [SD] −2.95–2.38 [1.47]) (Gray & Woodall, 1994). The logistic regression model was statistically significant, χ2 (58) = 422, p < 0.001. The model explained 27.2% (Nagelkerke R2) of the variance in pathway outcome and correctly classified 72.9% of cases. Two facilities (Facilities 4 and 5) had insufficient numbers and were excluded from analysis for Models 2 and 3. 4 DISCUSSION The evaluation showed the refined service metrics further explained disparity in the primary service outcome between N/OPSC&MDS facilities, but only partially. Overall, 10/18 facilities (compared to 15 in the previous study) remained significantly different to the referent facility for the primary service outcome, together with higher explained variance (49.7% from 32% previously). The small variation in the primary clinical outcome remained the same as the previous study (only 3/18 facilities) but with slightly improved explained variance (27.2% from 20% previously) (Raymer et al., 2021). Although sustained benefits of the service over an extended time are evident by the very similar primary service (72.5% discharged, previously 69.4%) and clinical (67.1% clinically meaningful response, previously 68.9%) outcomes of the current and previous audits (Raymer et al., 2021), the persisting variation between facilities irrespective of refined service metrics needs to be understood. When incorporating the revised service metrics, disparity between facilities in the primary service outcome was still inflated (albeit by 1 facility) when adjusted for service-related variables. Notably findings showed most service-related metrics significant in the final primary service model were also significant in the previous study (Management Duration, Triage Category, Non-Attendance, and Medical Specialist Input during N/OPSC management) (Raymer et al., 2021). Only two newly included service-related variables were significant in the final service model (Multidisciplinary Team Referral, Initiation of Investigations). From the perspective of future service planning this persisting variation between facilities in the primary service outcome potentially reflects some differences in service provision between N/OPSC&MDS facilities. In response a collaborative project is now underway between N/OPSC&MDS facilities aimed at identifying and addressing differences in service provision and unwarranted variability between facilities. The mixed methods project will gain a deeper understanding of service provision and associated service-related variables from a facility perspective. It is anticipated this deeper insight will facilitate adjustment of any modifiable service-related characteristics and service provision, closing the gap between outcomes achieved between facilities. Overall the findings of this study further highlight challenges in capturing relevant, high quality musculoskeletal data. Yet in the interests of patient care, continual refinement of service metrics remains a priority to permit meaningful benchmarking (Burgess et al., 2022) not only within multi-facility services such as the N/OPSC&MDS, but also between musculoskeletal services nationally and internationally. AUTHOR CONTRIBUTIONS All authors contributed to the concept of the study, the acquisition, analysis and interpretation of data, and were involved in draughting of the manuscript. ACKNOWLEDGEMENTS The investigators would like to thank the clinical and administrative staff of the Neurosurgical and Orthopaedic Physiotherapy Screening Clinics and Multidisciplinary Service (N/OPSC & MDS) facilities across Queensland, Australia, who completed service metrics populating the service database. Open access publishing facilitated by The University of Queensland, as part of the Wiley - The University of Queensland agreement via the Council of Australian University Librarians. CONFLICTS OF INTEREST The authors have no conflicts of interest to declare. ETHICS STATEMENT The project received ethical approval by the institute's ethical review committee (HREC/17/QRBW/154). Open Research DATA AVAILABILITY STATEMENT Research data are not shared. The dataset from this study is not publicly available due to the data having been collated from multiple hospital health services each with individual data custodians that require further approval for access. Please contact corresponding author ([email protected]) regarding any data requests. Supporting Information Filename Description msc1717-sup-0001-suppl-data.docx52.2 KB Supporting Information S1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. REFERENCES ABS. (2018). What is SEIFA? Canberra. Australian Bureau of Statistics (ABS). http://www.abs.gov.au/ausstats/[email protected]/Lookup/2033.0.55.001main+features4201 ACSQHC. (2013). Australian commission on safety and quality in health care (ACSQHC). Medical practice variation: Background paper. ACSQHC. Beaton, D. E., Wright, J. G., & Katz, J. N. (2005). Development of the QuickDASH: Comparison of three item-reduction approaches. Journal of Bone and Joint Surgery, 87(5), 1038– 1046. https://doi.org/10.2106/jbjs.d.02060 Binkley, J. M., Stratford, P. W., Lott, S. A., & Riddle, D. L. (1999). The lower extremity functional scale (LEFS): Scale development, measurement properties, and clinical application. North American orthopaedic rehabilitation research network. Physical Therapy, 79, 371– 383. Box, G. E. P., & Tidwell, P. W. (1962). Transformation of the independent variables. Technometrics, 4, 531– 550. https://doi.org/10.1080/00401706.1962.10490038 Burgess, R., Lewis, M., & Hill, J. C. (2022). Benchmarking community/primary care musculoskeletal services: A narrative review and recommendation. Musculoskeletal Care, 1– 11. https://doi.org/10.1002/msc.1676 Chiarotto, A., Vanti, C., Cedraschi, C., Ferrari, S., de Lima, E. S. R. F., Ostelo, R. W., & Pillastrini, P. (2016). Responsiveness and minimal important change of the pain self-efficacy Questionnaire and short forms in patients with chronic low back pain. The Journal of Pain, 17(6), 707– 718. https://doi.org/10.1016/j.jpain.2016.02.012 Clement, R. C., Welander, A., Stowell, C., Cha, T. D., Chen, J. L., Davies, M., Fairbank, J. C., Foley, K. T., Gehrchen, M., Hagg, O., Jacobs, W. C., Kahler, R., Khan, S. N., Lieberman, I. H., Morisson, B., Ohnmeiss, D. D., Peul, W. C., Shonnard, N. H., Smuck, M. W., … FRitzell, P. (2015). A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthopaedica, 86(5), 523– 533. https://doi.org/10.3109/17453674.2015.1036696 Cottrell, M. A., O'Leary, S. P., Swete-Kelly, P., Elwell, B., Hess, S., Litchfield, M. A., McLoughlin, I., Tweedy, R., Raymer, M., Hill, A. J., & Russell, T. G. (2018). Agreement between telehealth and in-person assessment of patients with chronic musculoskeletal conditions presenting to an advanced-practice physiotherapy screening clinic. Musculoskelet Sci Pract, 38, 99– 105. https://doi.org/10.1016/j.msksp.2018.09.014 Fan, Z. J., Smith, C. K., & Silverstein, B. A. (2008). Assessing validity of the QuickDASH and SF-12 as surveillance tools among workers with neck or upper extremity musculoskeletal disorders. Journal of Hand Therapy, 21(4), 354– 365. https://doi.org/10.1197/j.jht.2008.02.001 Fehring, T. K. (2016). AAHKS risk adjustment initiative: Why is it important? The Journal of Arthroplasty, 31(6), 1148– 1150. https://doi.org/10.1016/j.arth.2016.02.083 Fennelly, O., Blake, C., Desmeules, F., Stokes, D., & Cunningham, C. (2018). Patient-reported outcome measures in advanced musculoskeletal physiotherapy practice: A systematic review. Musculoskeletal Care, 16(1), 188– 208. https://doi.org/10.1002/msc.1200 Field, A. P. (2009). Discovering statistics using SPSS: (and sex and drugs and rock 'n' roll). SAGE. Fries, J., Spitzer, P., Kranes, G., & Holman, H. (1980). Measurement of patient outcome in arthritis. Arthritis & Rheumatism, 23(2), 137– 145. https://doi.org/10.1002/art.1780230202 Gray, J. B., & Woodall, W. H. (1994). The maximum size of standardized and internally studentized residuals in regression analysis. The American Statistician, 48(2), 111– 113. https://doi.org/10.2307/2684258 Hagg, O., Fritzell, P., Nordwall, A., & Swedish Lumbar Spine Study, G. (2003). The clinical importance of changes in outcome scores after treatment for chronic low back pain. European Spine Journal, 12(1), 12– 20. https://doi.org/10.1007/s00586-002-0464-0 Hill, J. C., Garvin, S., Chen, Y., Cooper, V., Wathall, S., Saunders, B., Lewis, M., Protheroe, J., Chudyk, A., Dunn, K. M., Hay, E., van der Windt, D., Mallen, C., & Foster, N. E. (2020). Stratified primary care versus non-stratified care for musculoskeletal pain: Findings from the STarT MSK feasibility and pilot cluster randomized controlled trial. BMC Family Practice, 21(1), 30. https://doi.org/10.1186/s12875-019-1074-9 ICHOM. (2017). ICHOM hip & knee osteoarthritis data collection reference guide. https://ichom.org/files/medical-conditions/hip-knee-osteoarthritis/hip-knee-osteoarthritis-reference-guide.pdf Kamper, S. J., Maher, C. G., & Mackay, G. (2009). Global rating of change scales: A review of strengths and weaknesses and considerations for design. Journal of Manual & Manipulative Therapy, 17(3), 163– 170. https://doi.org/10.1179/jmt.2009.17.3.163 Lauridsen, H. H., Hartvigsen, J., Manniche, C., Korsholm, L., & Grunnet-Nilsson, N. (2006). Responsiveness and minimal clinically important difference for pain and disability instruments in low back pain patients. BMC Musculoskelet Disord, 7(1), 82. https://doi.org/10.1186/1471-2474-7-82 Moretto, N., Comans, T. A., Chang, A. T., O'Leary, S. P., Osborne, S., Carter, H. E., Smith, D., Cavanagh, T., Blond, D., & Raymer, M. (2019). Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services. Implementation Science, 14(1), 78. https://doi.org/10.1186/s13012-019-0923-1 Nicholas, M. K., McGuire, B. E., & Asghari, A. (2015). A 2-item short form of the pain self-efficacy Questionnaire: Development and psychometric evaluation of PSEQ-2. The Journal of Pain, 16(2), 153– 163. https://doi.org/10.1016/j.jpain.2014.11.002 Partington, A., Chew, D. P., Ben-Tovim, D., Horsfall, M., Hakendorf, P., & Karnon, J. (2017). Screening for important unwarranted variation in clinical practice: A triple-test of processes of care, costs and patient outcomes. Australian Health Review, 41(1), 104– 110. https://doi.org/10.1071/ah15101 Raymer, M., Mitchell, L., Window, P., Cottrell, M., Comans, T., & O'Leary, S. (2021). Disparities in service and clinical outcomes in state-wide advanced practice physiotherapist-led services. Healthcare, 9(3), 278. https://doi.org/10.3390/healthcare9030278 Riddle, D. L., & Stratford, P. W. (1998). Use of generic versus regional specific functional status measures on patients with cervical spine disorders. Physical Therapy, 78(9), 951– 963. https://doi.org/10.1093/ptj/78.9.951 Sangha, O., Stucki, G., Liang, M. H., Fossel, A. H., & Katz, J. N. (2003). The self-administered comorbidity Questionnaire: A new method to assess comorbidity for clinical and health services research. Arthritis & Rheumatism, 49(2), 156– 163. https://doi.org/10.1002/art.10993 Tashjian, R. Z., Deloach, J., Porucznik, C. A., & Powell, A. P. (2009). Minimal clinically important differences (MCID) and patient acceptable symptomatic state (PASS) for visual analog scales (VAS) measuring pain in patients treated for rotator cuff disease. Journal of Shoulder and Elbow Surgery, 18(6), 927– 932. https://doi.org/10.1016/j.jse.2009.03.021 Tubach, F., Ravaud, P., Baron, G., Falissard, B., Logeart, I., Bellamy, N., Bombardier, C., Felson, D., Hochberg, M., van der Heijde, D., & Dougados, M. (2005). Evaluation of clinically relevant changes in patient reported outcomes in knee and hip osteoarthritis: The minimal clinically important improvement. Annals of the Rheumatic Diseases, 64(1), 29– 33. https://doi.org/10.1136/ard.2004.022905 Vernon, H., & Mior, S. (1991). The neck disability index: A study of reliability and validity. Journal of Manipulative and Physiological Therapeutics, 14, 409– 415. Williams, K., Sansoni, J., Morris, D., Grootemaat, P., & Thompson, C. (2016). Patient-reported outcome measures: Literature review. A. C. o. S. a. Q. i. H. Care. Yen, S. C., Corkery, M. B., Chui, K. K., Manjourides, J., Wang, Y. C., & Resnik, L. J. (2015). Risk adjustment for lumbar dysfunction: Comparison of linear mixed models with and without inclusion of between-clinic variation as a random effect. Physical Therapy, 95(12), 1692– 1702. https://doi.org/10.2522/ptj.20140444 Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
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