The perfect storm coming to healthcare: value-based healthcare meets fraud and abuse
Bibliographic record
Abstract
Purpose This manuscript addresses the emerging tension between healthcare providers and regulatory authorities as the delivery of healthcare transitions from Volume-based Healthcare to Value-based Healthcare (VBH). In Volume-based healthcare, the more patients a doctor sees, the more money she makes. The more expensive drugs and treatments the doctor provides, the more money she makes. In VBH, keeping patients healthy with a focus on patient outcomes offers the potential to deliver improved healthcare outcomes along with cost reduction. In Volume-based Healthcare, there is an incentive to induce referrals and offer remuneration seeking referrals subjecting healthcare providers to Fraud and Abuse and Antikickback regulations. In VBH, there is no incentive to do more to achieve more income. The problem is: where do you draw the line between helping providers and patients by offering services and goods that achieve quality and cost reduction without running afoul of the law. This emerging tension is exacerbated by the emergence of social determinants of healthcare (SDOH) that have more to do with the quality of one’s healthcare than direct clinical care, medicines and medical devices. Design/methodology/approach This manuscript is based entirely on a narrative review and secondary research. No primary research has been conducted. Findings VBH offers the potential to achieve The Triple Aim: improve patient healthcare outcomes; enhance patient access to healthcare and satisfaction; and reduce costs. SDOH such as poverty, food deserts, crime, education, homelessness, transportation and more have more of an impact on the quality of one’s healthcare than direct clinical healthcare. Healthcare marketers can move beyond just selling goods and services to offering value that addresses the SDOH that stand in the way of achieving good health. Research limitations/implications This emerging approach to healthcare delivery is relatively new. Government has set the theme for this transformation by announcing a “regulatory sprint toward value-based healthcare”. The regulatory authorities like the Department of Justice, The Office of Inspector General (OIG) and State Attorneys General recognize that Fraud and Abuse and Antikickback can be obstacles to providing VBH. This new approach to healthcare delivery formally launched in January 2021 so there is little research on strategy and marketing guidance. Practical implications The varied healthcare providers such as hospitals, doctors, nurses, pharmacists and contractual healthcare networks such as Accountable Care Organizations and Clinically Integrated Networks are just beginning to move forward on this new paradigm. Social implications Social implications are huge. SDOH provide a real-world context in attempting to achieve improved healthcare. Take the example of an older patient with Type 2 diabetes along with a number of additional comorbidities such as obesity, depression, and more. The patient needs insulin for her diabetes, but she is homeless and lives under a bridge. What good is the best doctor, best hospital, best medicines if the patient is homeless. Originality/value The research on this healthcare delivery transition is just beginning to emerge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".