Hospital Readmissions Reduction Program does not provide the right incentives: issues and remedies

The Hospital Readmissions Reduction Program (HRRP) reduces Medicare payments to hospitals with higher-than-expected readmission rates where the expected readmission rate for each hospital is determined based on the readmission levels at other hospitals. Although similar relative-performance-based schemes are shown to lead to socially optimal outcomes in other settings (e.g., cost cutting efforts), HRRP differs from these schemes in three respects: (i) deviation from the targets are adjusted using a multiplier; (ii) the total financial penalty for a hospital with higher-than-expected readmission rate is capped; and (iii) hospitals with lower-than-expected readmission rates do not receive bonus payments. We study three regulatory schemes derived from HRRP to determine the impact of each feature, and use a principal-agent model to show that: (i) HRRP over-penalizes hospitals with excess readmissions because of the multiplier and its effect can be substantial; (ii) having a penalty cap can curtail the effect of financial incentives and result in a no-equilibrium outcome when the cap is too low; and (iii) not allowing bonus payments leads to many alternative symmetric equilibria, including one where hospitals exert no effort to reduce readmissions. These results show that HRRP does not provide the right incentives for hospitals to reduce readmissions. Next we show that a bundled payment type reimbursement method, which reimburses hospitals once for each episode of care (including readmissions), leads to socially optimal cost and readmissions reduction efforts. Finally we show that, when delays to accessing care are inevitable, the reimbursement schemes need to provide additional incentives for hospitals to invest sufficiently in capacity

A Theoretical Analysis of the Lean Startup Method

The lean start-up method (LSM) advocates an iterative and adaptive product development and testing approach to innovation. It recommends firms to build test products, use them to learn about consumer preferences, and modify (or “pivot”) the product design accordingly. It is less straightforward to understand how effective LSM can be, however, not least because consumers’ responses to the test product are influenced by its quality, price, and design—that is, learning is endogenous to the features of the test product. This paper analyzes the build-test-learn cycle of LSM using an analytical model to understand its microfoundation and how best to implement it. We find that an optimal test product that maximizes learning should aim either to confirm a more likely product design or to rule out a less likely product design as being the most desired by consumers, have a vertical quality that is neither too high nor too low, and have a higher quality when aiming to confirm than to rule out. We also identify the product-market conditions for which the LSM is more effective. Conceptualizing the LSM via a formal model may help to improve its implementation in practice and to advance further academic research

Making the Invisible Visible: Intelligent Recovery Monitoring of Aortic Arch Repair Surgery Proposal

Thoracic pain is a very frequent reason for consultation in the primary care nursing consultation. However, when the healthcare professional is facing a patient with intense and tearing pain in the chest that induces him to think that he is facing a possible aortic dissection, then it is in an emergency where the patient requires immediate attention and a referral without loss of time to a cardiac surgery unit. This study aims to publicize the misfortunes that may occur in the patient during the recovery of aortic arch repair surgery. The results were obtained through the analysis of the clinical history of patients with aortic pathology, all of them operated in the cardiac surgery unit of the Virgen de la Salud Hospital of Toledo (CHT) Spain. We are proposing a continuous monitoring solution that can ascertain the life quality of patients that went arch repair surgery. Life quality is difficult to measure quantitatively. We suggest threshold levels for a complex dataset that, when considered simultaneously through data fusion techniques applied with reinforcement learning algorithms can have a numeric output for quality of life as a whole. In this groundbreaking paper, the fundaments of the ontological structure for data acquisition, model definition, data acquisition and reasoning based in deep learning techniques are introduced

Prognostic factors in medically inoperable early stage lung cancer patients treated with stereotactic ablative radiation therapy (SABR): Turkish Radiation Oncology Society Multicentric Study

Objective We identified factors influencing outcomes in patients with medically inoperable early stage lung cancer (MIESLC) treated with stereotactic ablative radiation therapy (SABR) at 14 centers in Turkey. Materials and Methods We retrospectively analyzed 431 patients with stage I-II MIESLC treated with SABR from 2009 through 2017. Age; sex; performance score; imaging technique; tumor histology and size; disease stage radiation dose, fraction and biologically effective dose with an alpha/beta ratio of 10 (BED10); tumor location and treatment center were evaluated for associations with overall survival (OS), local control (LC) and toxicity. Results Median follow-up time was 27 months (range 1-115); median SABR dose was 54 Gy (range 30-70) given in a median three fractions (range 1-10); median BED(10)was 151 Gy (range 48-180). Tumors were peripheral in 285 patients (66.1%), central in 69 (16%) and 100 Gy (P = .011), adenocarcinoma (P = .025) and complete response on first evaluation (P = .007) predicted favorable LC. BED10> 120 Gy (hazard ratio [HR] 1.9, 95% confidence interval [CI] 1.1-3.2,P = .019) and tumor size ( 120 Gy was needed for better LC and OS for large, non-adenocarcinoma tumors

The Role of Local Intermediaries in the Process of Digitally Engaging Non-Users of the Internet

This article aims to provide a better understanding of the process of becoming digitally engaged. Those who cannot utilise digital networks are systematically disadvantaged, particularly in a hyper-connected world in which services are provided online by default. By interviewing and observing clients and trainers at a telecentre, the ACT Digital Hub, this study investigated the process that non-internet users undergo-from digital readiness to digital engagement-in order to become adept users. Intermediaries such as telecentres play a crucial role in equipping non-users with digital readiness, which is a precursor to digital media literacy. Social environment also plays a significant role in non-users' digital readiness. Rather than focusing merely on the provision of access to bridge the digital divide, we need a longer-term investment in adequate environments, such as sustainable community training centres, that nurture digital readiness.</p