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The concept of physicians referring patients to their own healthcare entities is considered a "self-referral". A discerning factor of a self-referral is when the physician has a financial interest in the entity of patient referral. Prospects of healthcare overutilization and costs, thereby, rise. Self-referral laws, therefore, are important to regulate overutilization and contain costs. In the 1980s, Congressman Fortney Stark initiated an act that was one of the precursors to one such self-referral law, known as the Stark Law. The Stark Law, in its initial phase, known as Stark I, addressed self-referrals selectively from laboratory services. Stark I, thereafter, in a series of subsequent amendments and enactments, burgeoned to include multiple services, referred as Designated Health Services (DHS), for self-referrals. The expanded law, inclusive of those DHS, is now known as Stark II. The passage of the 2010 Affordable Care Act as well as the prevailing 2019 Coronavirus Disease (COVID-19) pandemic further modified the Stark Law. Given the legislative history of the said law, the present review curates the legal initiatives of this law from its nascent formative stages to the present form. The purpose of the above curation is to present a bird's eye view of its evolution and present analysts of any future research segments. This review, furthermore, describes the waivers of this law specific to COVID-19, or COVID-19 blanket waivers, which are instruments to assuage any barriers and further placate any hurdles arising from this law prevalent in this pandemic.

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The Emergency Medical Treatment & Active Labor Act (EMTALA) is a healthcare law specific to screening, stabilizing, and transferring (or accepting) patients with emergency medical conditions and active labor. This law, contextual to Medicare-participating hospitals, ensures public access to emergency medical services, regardless of the individual's ability to pay. The Defensive Medicine (DM) model and Physician Responsiveness to Standard-of-care Reforms (PRSRs) model are two medical malpractice frameworks leveraged in this paper. The nodes of these frameworks comprise of the treatment-versus-no-treatment dynamics and cutoff thresholds. Cutoff thresholds are specific to health risks and treatment price rates. Health risks stem from those with treating or not treating a patient as well as those inherent from the patient's ailment. Treatment price rates are subcategorized into customary and efficient price rates. Given the above nodes of these frameworks, this paper examines how the above medical malpractice models synchronize and sequentially align with the legal obligations of this law. This paper, furthermore, contemplatively describes how the incentivize/penalize dynamics interrelate to the push/pull dynamics of the PRSRs malpractice model. Thereafter, this paper applies the above push/pull dynamics contextual to the three specific obligations of this law, essentially, screening, stabilizing, and transferring (or accepting) emergency care patients. Conclusively, this paper illustrates the above network in a cascading algorithm that ligates the nodes of these frameworks to EMTALA's obligations.

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The definition of defensive medicine has evolved over time given various permutations and combinations. The underlying meaning, however, has persisted in its relevance towards two classifications, positive and negative defensive medicine. Positive defensive medicine is specific to overutilization, excessive testing, over-diagnosing, and overtreatment. Negative defensive medicine, on the contrary, is specific to avoiding, referring, or transferring high risk patients. Given the above bifurcation, the present research analyzes defensive medicine in the landscape of medical errors. In its specificity to medical errors, we consider the cognitive taxonomies of medical errors contextual to execution and evaluation slips and mistakes. We, thereafter, illustrate how the above taxonomy interclasps with five classifications of medical errors. These classifications are those that involve medical errors of operative, drug-related, diagnostic, procedure-related, and other types. This analytical review illustrates the nodular frameworks of defensive medicine. As furtherance of our analysis, this review deciphers the above nodular interconnectedness to these error taxonomies in a cascading stepwise sequential manner. This paper was designed to elaborate and to stress repeatedly that practicing defensive medicine entails onerous implications to physicians, administrators, the healthcare system, and to patients. Practicing defensive medicine, thereby, is far from adhering to those optimal healthcare practices that support quality of care metrics/milestones, and patient safety measures. As an independent standalone concept, defensive medicine is observed to align with the taxonomies of medical errors based on this paper's diagrammatic and analytical inference.

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Background: Following the 2015 repeal of the Sustainable Growth Rate formula, the US Centers for Medicare & Medicaid Services' formula under which physicians were reimbursed, two payment systems were put in place to incentivize physicians, one of which was the Merit-based Incentive Payment System (MIPS). MIPS emphasizes high-quality care that is accessible, affordable, and supports a healthier population. Objectives: This research aims to measure characteristics of MIPS relevant to National Quality Strategy (NQS) domains, quality measure types, and clinical specialties; categorize MIPS with NQS domains and quality measure types by MIPS specialty types; and quantify the relationship between MIPS specialties, measure types, and two NQS domains, Effective Clinical Care (ECC) and Efficiency/Cost Reduction (E/CR), for years 2017 through 2020. Methodology: The Pearson's chi-square test examined distributions of the analyzed categorical variables. The Categorical Dependent Variable Method examined the association between the dependent and independent variables. Results: The Pearson's chi-square test showed statistically significant distributions between ECC and E/CR when analyzed with the types of quality measures. There were more process measures (93.81% vs 89.64% [P=.000]) in 2018 versus 2017. This changed minutely with significantly less process measures (93.75% vs 93.81% [P=.000]) in 2019 versus 2018. Finally, measure types changed minutely but significantly with less process measures (93.81% vs 93.75% [P=.000]) in 2020 versus 2019. The regression model showed that ECC was significantly associated with outcome measures through all analyzed years of this research. Conclusion: The above findings show scope for including additional outcome measures, given its importance in MIPS. There is potential to increase the percentage allocation for reporting more outcome measures in quality. This re-allotment infers reporting more outcome measures aligning with priority outcome measures (PROMs). Re-allocating the incentive formula to report more outcome measures aligned with PROMs shows potential to increase reporting of more outcome measures under MIPS.

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Patient safety is one of the overarching goals of patient care and quality management. Of the many quality management frameworks, Beauchamp and Childress's four principles of biomedical ethics presents aspects of patient centeredness in clinical care. The Institute of Medicine's six aims for improvement encapsulates elements of high-quality patient care. The Institute of Healthcare Improvement's Triple Aim focuses on three aspects of care, cost, and health. Given the above frameworks, the present review was designed to emphasize the initiatives the system has taken to address various efforts of improving quality and patient safety. We, hereby, present a contemplative review of the concepts of informed consent, informed refusal, healthcare laws, policy programs, and regulations. The present review, furthermore, outlines measures and policies that management and administration implement and enforce, respectively, to ensure patient centered care. We, conclusively, explore prototype policies such as the Delivery System Reform Incentive Payment Program that imbues the elements of quality management frameworks, Hospital-Acquired Conditions Reduction Program that supports patient safety, and Hospital Readmissions Reduction Program that focuses on curbing readmissions.

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INTRODUCTION: Adoption of Medicaid Section 1115 waiver is one of the many ways of innovating healthcare delivery system. The Delivery System Reform Incentive Payment (DSRIP) pool, one of the two funding pools of the waiver has four categories viz. infrastructure development, program innovation and redesign, quality improvement reporting and lastly, bringing about population health improvement. BACKGROUND: A metric of the fourth category, preventable hospitalization (PH) rate was analyzed in the context of eight conditions for two time periods, pre-reporting years (2010-2012) and post-reporting years (2013-2015) for two hospital cohorts, DSRIP participating and non-participating hospitals. The study explains how DSRIP impacted Preventable Hospitalization (PH) rates of eight conditions for both hospital cohorts within two time periods. METHODS: Eight PH rates were regressed as the dependent variable with time, intervention and post-DSRIP Intervention as independent variables. PH rates of eight conditions were then consolidated into one rate for regressing with the above independent variables to evaluate overall impact of DSRIP. An interrupted time series regression was performed after accounting for auto-correlation, stationarity and seasonality in the dataset. RESULTS: In the individual regression model, PH rates showed statistically significant coefficients for seven out of eight conditions in DSRIP participating hospitals. In the combined regression model, the coefficient of the PH rate showed a statistically significant decrease with negative p-values for regression coefficients in DSRIP participating hospitals compared to positive/increased p-values for regression coefficients in DSRIP non-participating hospitals. CONCLUSION AND IMPLICATIONS: Several macro- and micro-level factors may have likely contributed DSRIP hospitals outperforming DSRIP non-participating hospitals. Healthcare organization/provider collaboration, support from healthcare professionals, DSRIP's design, state reimbursement and coordination in care delivery methods may have led to likely success of DSRIP. LEVEL OF EVIDENCE: IV, a retrospective cohort study based on longitudinal data.

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Texas is one of 8 states that have received a Medicaid 1115 Transformation Waiver in which federal supplemental payments are being used to incentivize delivery system reform. Under the Texas Transformation Waiver's 5-year Delivery System Reform Incentive Payment (DSRIP) program, hospitals and other providers have established regional health care partnerships, conducted regional needs assessments, and developed and implemented projects addressing local gaps in service. The projects were selected from menus, supplied by the Texas Health and Human Services Commission and the Centers for Medicare & Medicaid Services, which defined acceptable infrastructure development and/or program innovation and redesign initiatives. Providers receive payment for planning the projects and achieving metrics and milestones related to project implementation and performance. This article describes the major features of the Texas DSRIP model and the resulting implementation and performance to date in the most populous region of the state.

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