| Literature DB >> 28532470 |
Nieke A Elbers1, Robin Chase2, Ashley Craig3, Lyn Guy4, Ian A Harris5, James W Middleton3, Michael K Nicholas3, Trudy Rebbeck3,6, John Walsh3, Simon Willcock7, Keri Lockwood3, Ian D Cameron3.
Abstract
BACKGROUND: Problems may arise during the approval process of treatment after a compensable work injury, which include excess paperwork, delays in approving services, disputes, and allegations of over-servicing. This is perceived as undesirable for injured people, health care professionals and claims managers, and costly to the health care system, compensation system, workplaces and society. Introducing an Evidence Based Medicine (EBM) decision tool in the workers' compensation system could provide a partial solution, by reducing uncertainty about effective treatment. The aim of this study was to investigate attitudes of health care professionals (HCP) to the potential implementation of an EBM tool in the workers' compensation setting.Entities:
Keywords: Evidence-based medicine; Guidelines; Health care practitioners; Workers’ compensation process
Mesh:
Year: 2017 PMID: 28532470 PMCID: PMC5440905 DOI: 10.1186/s12911-017-0460-2
Source DB: PubMed Journal: BMC Med Inform Decis Mak ISSN: 1472-6947 Impact factor: 2.796
Participant characteristics (n = 231)a
| Main category | Sub category |
|
|---|---|---|
| Age | 18–30 years | 17 (7%) |
| 31–40 years | 43 (19%) | |
| 41–50 years | 54 (23%) | |
| 51–60 years | 79 (34%) | |
| >60 years | 37 (16%) | |
| Sex | Female | 83 (36%) |
| Male | 147 (64%) | |
| Clinical specialty | Chiropractic | 31 (13%) |
| Clinical psychology | 36 (16%) | |
| General practice | 15 (6%) | |
| Injury management | 14 (6%) | |
| Musculoskeletal & Occupational Medicine & Rheumatology | 26 (11%) | |
| Pain & Rehabilitation Medicine | 31 (13%) | |
| Physiotherapy | 37 (16%) | |
| Surgery | 39 (17%) | |
| Work hours | Full time | 58 (25%) |
| Part time | 172 (75%) | |
| Clinical setting - geographical | Urban | 169 (73%) |
| Rural | 36 (16%) | |
| Both | 25 (11%) | |
| Clinical setting - type | Public hospital | 27 (12%) |
| Private hospital | 20 (9%) | |
| Community | 96 (42%) | |
| Multiple settings | 87 (38%) | |
| Work experience | <10 years | 41 (18%) |
| 10–20 years | 73 (32%) | |
| >20 years | 116 (50%) | |
| Providing workers’ compensation services | No | 39 (17%) |
| Yes | 191 (83%) |
a231 participants were included, of which one participant did not complete the demographic characteristics
Evidence Based Medicine perceptions
| Mean (SD) | |
|---|---|
| 1. EBM practice | |
| What percentage of the treatments you recommend and/or procedures you undertake is evidence based? | 75.8 (20.0) |
| 2. Knowledge | |
| a. I am | 4.4 (0.7) |
| b. I am | 4.3 (0.6) |
| c. I have enough access to information about evidence based practices | 4.0 (0.9) |
| d. I have/make time to keep myself up to date with evidence base practices | 3.9 (0.9) |
| e. I am able to interpret the evidence base from the literature | 4.1 (0.8) |
| Mean knowledge score: | 4.2 (0.6) |
| 3. Attitudes | |
| a. I feel confident that I can perform evidence based practice | 4.2 (0.7) |
| b. I believe that evidence based practice leads to improved patient outcomes | 4.1 (0.9) |
| c. I am motivated to adopt evidence based practice | 4.2 (0.8) |
| Mean attitudes score: | 4.2 (0.7) |
| 4. Behaviour | |
| a. It is easy to apply evidence based treatment in my day to day practice | 3.5 (1.0) |
| b. I am able to reconcile patient preferences with evidence based practice | 3.7 (0.8) |
| c. There are enough resources/facilities (e.g. staff, educational material) to adhere to evidence based practice | 3.4 (1.0) |
| d. I have enough time to apply evidence based treatment | 3.7 (1.0) |
| e. My colleagues are supportive of the evidence base in my field | 3.7 (1.0) |
| f. In general, in my clinical field, payment systems can influence the decisions about treatment | 3.3 (1.2) |
| Mean behaviour score: | 3.6 (0.6) |
The scales for items 2a to 4f ranged from 1–5 (1 = strongly disagree, 5 = strongly agree)
Fig. 1Percentage of clinical practice self-reported adherence to EBM, divided by clinical specialties. Horizontal line displays the average percentage across all clinical specialties (67%)
Mean, Standard Deviation and Median per clinical specialty on item about payment system influencing decisions
| In general in my field payment systems can influence decisions about treatment | Mean (SD) | Median |
|---|---|---|
| Chiropractic | 3.1 (1.1) | 3.0 |
| Clinical psychology | 3.4 (1.2) | 4.0 |
| General practise | 3.4 (1.4) | 4.0 |
| Injury management | 3.9 (1.0) | 4.0 |
| Musculoskeletal/Occupational Medicine/Rheumatology | 3.0 (1.3) | 3.0 |
| Pain & Rehabilitation Medicine | 3.7 (0.9) | 4.0 |
| Physiotherapy | 3.4 (1.4) | 3.0 |
| Surgery | 3.2 (1.2) | 3.0 |
SD = Standard deviation
Differences between demographic/job characteristics and EBM adherence
| Demographic and job characteristic | Kruskal-Wallis H (df) or Mann–Whitney |
|---|---|
| Age (18–30/31–40/41–50/51–60/>60) | H (4) = 2.3, |
| Sex (Female/Male) | U = 5.6, |
| Years of work experience (<10/10–20/>20) | H (2) = 1.3, |
| Work hours (Full time/Part time) | U = 5.1, |
| Clinical setting (Urban/Rural/Both) | H (2) = 0.3, |
| Clinical setting (Public/Private/Community/Multiple) | H (3) = 0.9, |
| Providing services to workers’ compensation system (Yes/No) | U = 3.7, |
Characteristics of interviewees (n = 15)
| Characteristic | Subclass | Number |
|---|---|---|
| Sex | Female | 6 |
| Male | 9 | |
| Age group | 30 to 39 | 3 |
| 40 to 49 | 3 | |
| 50 to 59 | 6 | |
| 60 plus | 3 | |
| Work hours (clinical work) | Part-time | 6 |
| Full-time | 9 | |
| Work experience in clinical practice | <10 years | 3 |
| 10–20 years | 4 | |
| >20 years | 8 | |
| Professional background | Physiotherapy | 5 |
| Psychology | 2 | |
| General practice | 1 | |
| Injury management | 1 | |
| Occupational medicine | 1 | |
| Pain & Rehab | 3 | |
| Surgery | 2 | |
| Clinical setting – geographical | Urban | 11 |
| Rural | 1 | |
| Both | 3 | |
| Clinical setting – type | Public | 3 |
| Private | 6 | |
| Public & Private | 4 | |
| Community | 2 |
Summary of findings about EBM tool in the workers’ compensation setting
| Issue | Comment |
|---|---|
| Trust and guidance for clinicians | Automatically approval of treatment could be perceived as a sign that that the claims managers trust the clinicians’ judgement |
| Limiting over-servicing | Recommendations about MRIs and certain surgeries could limit over-servicing, although solicitors could still get it anyway |
| Patient expectations and claimant monitoring | Tool may assist with managing patient expectations, as it sets timeframes about RTW |
| Individual differences & psychosocial factors | Tool may inadequately consider individual differences and psychosocial factors. Most patients are not one size fits all |
| Clinical judgement | Clinical judgement is important. Tool should not overpass clinical judgement |
| Patient preference | Some patients prefer to have non evidence based treatments but in general those patients can be convinced to value EBM |
| Quality of evidence | For many topics the evidence is not replicated, or very specific to certain populations |
| Quantity of evidence | Lack of evidence should not imply denial of treatment |
| Timeliness and risk assessment | Tool may not adequately assess risk of prolonged recovery, and therefore not sufficiently focus on timely treatment |
| Critical appraisal and guideline development | Interpretation of evidence is dependent on who does the interpretation. American tool might not be applicable in Australia |
| Claims managers using the EBM tool | Inexperience or limited training for claims managers could lead to rigid usage and unfair denials |
| Tool is no solution | Tool may not assist with the 20% most problematic cases, and does not recognise employer factors that prolong return to work |