Physiotherapists’ experiences of forensic audits by South African Medical Funding Schemes
Bibliographic record
Abstract
Background Forensic audits for billing irregularities have been conducted and researched in other countries, such as Australia, Canada, and the United States of America but not South Africa. Forensic auditors were reported to have been uncompromising in their belief that healthcare professionals were guilty of billing irregularities and had to be harshly penalised. The forensic investigators disregarded any valid reasons for irregular billing patterns, with resultant unfair audit outcomes. Small practices were forced to close their doors, leaving patients without medical support in small towns. Medical practitioners who presented their cases to a court of law were vindicated. No other studies were found regarding billing irregularity audits conducted on medical professionals or physiotherapists in South Africa. The Health Professions Council of South Africa conducted fraud, waste, and abuse audits on reported physiotherapists which was documented, and a survey was conducted on medical schemes regarding their opinion of fraud, waste, and abuse amongst medical practitioners. Aim This study aimed to explore and describe physiotherapists’ experiences of forensic audits conducted by medical funding scheme in South Africa, in the private sector. Methods An explorative, descriptive qualitative study was conducted with audited private-practice physiotherapists. Ethical clearance and informed consent was obtained. Participants were sampled through purposive and snowball methods. A total of 14 participants were interviewed, reaching saturation. Participants were interviewed using 11 individual interviews and three group discussions. Thirteen participants attended the group discussion in groups of five, six, and two participants, respectively. Four interviewers were used as a form of bracketing, and a semi-structured interview guide was provided to maintain rigor. Data were analysed using open coding and axial coding to describe the themes and subthemes. Findings were verified through member checking. Findings Six themes emerged from the rich data collected. The first five themes describe the experiences physiotherapists had, namely: 1) “unfairly persecuted, judged, and penalised”; 2) “overpowered and oppressed”, 3) “naively entrapped between a rock and a hard place”, 4) “distressed with a knife over your head”, and 5) “detrimental and hurtful”. These five themes emphasised that participants experienced the audits as unfair. Participants further described the experience as being worse than a stressful impact event, which led to detrimental outcomes, such as attrition of physiotherapists through, amongst others, closure of practices, and emigration. The last theme, 6) “seeking remedies pre-emptively and preparedly”, was about how physiotherapists wanted to improve audit processes to reduce its negative impact on physiotherapists. Conclusion Physiotherapists experienced the forensic audits adversely and made recommendations on how these may be averted. Further studies may investigate how these audits may be tailored more constructively in curtailing adverse experiences of physiotherapists. If this study’s findings are corroborated, then solutions need to be implemented by stakeholders who have been tasked with protecting the profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".