The Internet of Medical Things (IoMT) and interoperable technologies have transformed how patient data affect medical care; such technological innovations revolutionize how healthcare organizations (HCOs) improve cost, quality, and access. New cyber risks, however, accompany developing cyber ecosystems. Although immediate data exchange is beneficial, risk arises from the IoMT's increased susceptibility to human influence. The success of quality care relies on protecting health information technology (HIT) against newly developing cyber vulnerabilities. Therefore, managers must be just as invested in their HCO's cybersecurity protocols as cybercriminals are in bypassing those protocols. This essay proposes a healthcare cyber resiliency model that leverages human and technical factors through a cycle of feedback and process improvement. It intends to equip healthcare administrators with the foundational philosophy necessary to secure their emerging technologies.

We explored how readmissions may result from patients' lack of access to aftercare services, failure to adhere to psychotropic medication plans, and inability to understand and follow hospital discharge recommendations. We also investigated whether insurance status, demographics, and socioeconomic status are associated with hospital readmissions. This study is important because readmissions contribute to increased personal and hospital expenses and decreased community tenure (the ability to maintain stability between hospital admissions). Addressing hospital readmissions will promote optimal discharge practices beginning on day one of hospital admission. The study examined the differences in hospital readmission rates for patients with a primary psychotic disorder diagnosis. Discharge data were drawn in 2017 from the Nationwide Readmissions Database. Inclusion criteria included patients aged 0-89 years who were readmitted to a hospital between less than 24 hr and up to 30 days from discharge. Exclusion criteria were principal medical diagnoses, unplanned 30-day readmissions, and discharges against medical advice. The sampling frame included 269,906 weighted number of patients diagnosed with a psychotic disorder treated at one of 2,355 U.S. community hospitals. The sample size was 148,529 unweighted numbers of patients discharged. In a logistic regression model, weighted variables were calculated and used to determine an association between the discharge dispositions and readmissions. After controlling for hospital characteristics and patient demographics, we found that the odds for readmission for routine and short-term hospital discharge dispositions decreased for home health care discharges, which indicated that home health care can prevent readmissions. The finding was statistically significant when controlling for payer type and patient age and gender. The findings support home health care as an effective option for patients with severe psychosis. Home health care reduces readmissions and is recommended, when appropriate, as an aftercare service following inpatient hospitalization and may enhance the quality of patient care. Improving healthcare quality involves optimizing, streamlining, and promoting standardized processes in discharge planning and direct transitions to aftercare services.

research professor and codirector, undergraduate business program, Stony Brook University, Stony Brook, New York Dr. Pitocco is the lead author of "Using Data Analytics to Improve Hospital Quality Performance," winner of ACHE's 2022 Edgar C. Hayhow Award for an outstanding article published in the Journal of Healthcare Management. The article is freely available through 2022 at www.jhmjournalonline.com. For more information regarding this column, contact Dr. Pitocco at [email protected]. The author declares no conflicts of interest.

Journal of Healthcare Management: May-June 2022 - Volume 67 - Issue 3 - p 140-144 doi: 10.1097/JHM-D-22-00075

As strategies emerge to off-load hospital systems and prevent readmissions amid the COVID-19 pandemic, pragmatic assessments of readmission risk become increasingly important. The simplified HOSPITAL score is an extensively validated tool that predicts 30-day potentially avoidable readmission (PAR). Scores of 0 to 4 predict a 30-day PAR risk of 6.4%, while scores ≥ 5 predict a 30-day PAR risk of 17.3%. Its role in patients with COVID-19 is unknown. Our goal was to assess the simplified HOSPITAL score's accuracy in patients with COVID-19 and explore outcomes related to a hospital-at-home program. Patients discharged following an admission for clinically symptomatic COVID-19 from two hospitals belonging to the same healthcare system in the Midwest were included. Those who died, discharged to hospice or an acute care hospital, whose length of stay was < 1 day, or who discharged against medical advice were excluded. The simplified HOSPITAL score was tabulated for included patients to predict their 30-day PAR risk. The Brier score was calculated to compare the observed rates of 30-day readmission with rates predicted by the simplified HOSPITAL score. Prediction models with a Brier score <.25 are considered useful. Among 612 patients, the overall 30-day PAR rate was 10.1%. Most patients (n = 522 [85.3%]) had simplified HOSPITAL scores of 0 to 4, and 41 (7.8%) of these patients were readmitted. Among the 90 patients (14.7%) with scores ≥5, 21 (23.3%) were readmitted. The Brier score was 0.088, indicating very good accuracy between the predicted readmission risk and observed readmissions. In patients with scores 0 to 4, readmissions were highest in those discharged to acute or subacute rehabilitation (10.4% [8/77]), intermediate in those discharged home (8.1% [32/394]), and lowest in those discharged to hospital at home (1.9% [1/51]). However, these differences did not reach statistical significance. The simplified HOSPITAL score was accurate in patients with COVID-19 and can be used to direct resources toward those predicted to be at increased risk for readmission and to assess outcomes from readmission reduction strategies. Hospitals at home may be a promising strategy to decrease readmissions in patients with COVID-19.

senior vice president, ambulatory operations, Cooper University Health Care, Camden, New Jersey The author declares no conflicts of interest.

Journal of Healthcare Management 66(5):p 328-331, September-October 2021. | DOI: 10.1097/JHM-D-21-00194

system director of accreditation and quality assurance, Lee Health, Fort Myers, Florida The author declares no conflicts of interest.

Accountable care organizations (ACOs) need confidence in their return on investment to implement changes in care delivery that prioritize seriously ill and high-cost Medicare beneficiaries. The objective of this study was to characterize spending on seriously ill beneficiaries in ACOs with Medicare Shared Savings Program (MSSP) contracts and the association of spending with ACO shared savings. The population included Medicare fee-for-service beneficiaries identified with serious illness (N = 2,109,573) using the Medicare Master Beneficiary Summary File for 100% of ACO-attributed beneficiaries linked to MSSP beneficiary files (2014-2016). Lower spending for seriously ill Medicare beneficiaries and risk-bearing contracts in ACOs were associated with achieving ACO shared savings in the MSSP. For most ACOs, the seriously ill contribute approximately half of the spending and constitute 8%-13% of the attributed population. Patient and geographic (county) factors explained $2,329 of the observed difference in per beneficiary per year spending on seriously ill beneficiaries between high- and low-spending ACOs. The remaining $12,536 may indicate variation as a result of potentially modifiable factors. Consequently, if 10% of attributed beneficiaries were seriously ill, an ACO that moved from the worst to the best quartile of per capita serious illness spending could realize a reduction of $1,200 per beneficiary per year for the ACO population overall. Though the prevalence and case mix of seriously ill populations vary across ACOs, this association suggests that care provided for seriously ill patients is an important consideration for ACOs to achieve MSSP shared savings.