Dissecting racial bias in an algorithm used to manage the health of populations.

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts

Mortality Rate in Veterans with Multiple Chronic Conditions

Background: Among patients with multiple chronic conditions, there is increasing appreciation of the complex interrelatedness of diseases. Previous studies have focused on the prevalence and economic burden associated with multiple chronic conditions, much less is known about the mortality rate associated with specific combinations of multiple diseases. Objective: Measure the mortality rate in combinations of 11 chronic conditions. Design: Cohort study of veteran health care users. Participants Veterans between 55 and 64 years that used Veterans Health Administration health care services between October 1999 and September 2000. Measurements: Patients were identified as having one or more of the following: COPD, diabetes, hypertension, rheumatoid arthritis, osteoarthritis, asthma, depression, ischemic heart disease, dementia, stroke, and cancer. Mutually exclusive combinations of disease based on these conditions were created, and 5-year mortality rates were determined. Results: There were 741,847 persons included. The number in each group by a count of conditions was: none = 217,944 (29.34%); 1 = 221,111 (29.8%); 2 = 175,228 (23.6%); 3 = 86,447 (11.7%); and 4+ = 41,117 (5.5%). The 5-year mortality rate by the number of conditions was: none = 4.1%; 1 = 6.0%; 2 = 7.8%; 3 = 11.2%; 4+ = 16.7%. Among combinations with the same number of conditions, there was significant variability in mortality rates. Conclusions: Patients with multiple chronic conditions have higher mortality rates. Because there was significant variation in mortality across clusters with the same number of conditions, when studying patients with multiple coexisting illnesses, it is important to understand not only that several conditions may be present but that specific conditions can differentially impact the risk of mortality

Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management, and Costs

Persons with multiple chronic conditions are a large and growing segment of the US population. However, little is known about how chronic conditions cluster, and the ramifications of having specific combinations of chronic conditions. Clinical guidelines and disease management programs focus on single conditions, and clinical research often excludes persons with multiple chronic conditions. Understanding how conditions in combination impact the burden of disease and the costs and quality of care received is critical to improving care for the 1 in 5 Americans with multiple chronic conditions. This Medline review of publications examining somatic chronic conditions co-occurring with 1 or more additional specific chronic illness between January 2000 and March 2007 summarizes the state of our understanding of the prevalence and health challenges of multiple chronic conditions and the implications for quality, care management, and costs

Dissecting racial bias in an algorithm used to manage the health of populations.

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts

Survey shows that at least some physicians are not always open or honest with patients

The Charter on Medical Professionalism, endorsed by more than 100 professional groups worldwide and the US Accreditation Council for Graduate Medical Education, requires openness and honesty in physicians\u27 communication with patients. We present data from a 2009 survey of 1,891 practicing physicians nationwide assessing how widely physicians endorse and follow these principles in communicating with patients. The vast majority of physicians completely agreed that physicians should fully inform patients about the risks and benefits of interventions and should never disclose confidential information to unauthorized persons. Overall, approximately one-third of physicians did not completely agree with disclosing serious medical errors to patients, almost one-fifth did not completely agree that physicians should never tell a patient something untrue, and nearly two-fifths did not completely agree that they should disclose their financial relationships with drug and device companies to patients. Just over one-tenth said they had told patients something untrue in the previous year. Our findings raise concerns that some patients might not receive complete and accurate information from their physicians, and doubts about whether patient-centered care is broadly possible without more widespread physician endorsement of the core communication principles of openness and honesty with patients

Applying embedded program evaluation for care delivery transformation: An analysis of a home‐based urgent care program

Abstract Background In 2014, Mass General Brigham, formerly Partners HealthCare, launched a novel urgent home‐based medical care program to provide rapid medical evaluation and treatment to homebound patients and older adults with frailty or limited mobility named the partners mobile observation unit (PMOU) program. Methods We conducted a pragmatic, embedded evaluation assessing the impact of PMOU on postreferral utilization and total medical expenditure (TME). We used propensity weighting and logistic regression to estimate the 30‐day adjusted odds ratios (ORs) of emergency department (ED) utilization and inpatient medical hospitalization for patients enrolled in PMOU (891 episodes of care) relative to those who were referred but not enrolled in the program (57 episodes of care) during the period of April 2017 to June 2018. We additionally conducted a difference‐in‐differences analysis assessing program impact on TME, comparing claims data 30 days pre/post referral. Results Despite positive trends, there were no statistically significant differences between the two groups with regard to postreferral ED visits or hospitalizations, with an OR of 0.83 (p = 0.56) and OR of 0.64 (p = 0.21), respectively. There was no statistically significant difference in pre/post referral TME for intervention relative to control episodes (p = 0.64). In post hoc analysis of control episodes, 75% received care elsewhere within 14 days of referral. Conclusion Although the results suggested positive trends, this analysis of this relatively mature program was unable to identify statistically significant reductions in ED visits, hospitalizations, or TME associated with the PMOU program. Future efforts to build home‐based urgent care programs or related programs targeting older adults with frailty or limited mobility should aim to improve patient targeting and identify opportunities to improve program operations and generate meaningful reductions in healthcare utilization and spending

Impact of dementia care training on nurse care managers’ interactions with family caregivers

Abstract Background Nurse care managers (NCM) operate through care management programs to provide care for persons living with dementia (PLWD) and interact regularly with their family caregivers; however, most do not receive formal instruction in dementia care or caregiver support. CRESCENT (CaReEcoSystem primary Care Embedded demeNtia Treatment) is a telephone-based dementia care intervention adapted from the Care EcoSystem model designed to equip NCMs with these tools. For this study, we aimed to measure intervention fidelity and understand how dementia care training impacted NCMs’ provision of dementia care management services during interactions with caregivers of PLWD. Methods We recruited 30 active NCMs; 15 were randomly assigned to receive training. For each nurse, we randomly selected 1–3 patients with a diagnosis of dementia in each nurse’s care during January-June 2021 for a total of 54 medical charts. To assess training uptake and fidelity, we identified documentation by NCMs of CRESCENT protocol implementation in the medical records. To understand how the training impacted the amount and types of dementia care management services provided in interactions with family caregivers, we compared attention to key dementia topic areas between trained NCMs (intervention) and untrained NCMs (control). Results Within the trained group only, community resources for PLWD, followed by safety, medication reconciliation, and advanced care planning topic areas were addressed most frequently (> 30%), while behavior management was addressed least frequently (12%). Trained NCMs were more likely to document addressing aspects of caregiver wellbeing (p = 0.03), community resources (p = 0.002), and identification of behavior (p = 0.03) and safety issues (p = 0.02) compared to those without training. There was no difference between groups in the amount of care coordination provided (p = 0.64). Conclusion Results from this study demonstrate that focused dementia care training enriches care conversations in important topic areas for PLWD and family caregivers. Future research will clarify how best to sustain and optimize high quality dementia care in care management programs with special attention to the NCM-family caregiver relationship. Trial number NCT04556097

Gender differences in tobacco use in Africa, Asia, the Pacific, and Latin America

This paper reviews historical, anthropological and contemporary survey data concerning gender differences in tobacco use in Africa, Asia, the Pacific, and Latin America. In many cultural groups in these regions, tobacco use has been substantially more common among men than among women. In some groups, tobacco use has been about equally common for both sexes. No evidence was found of any group in which tobacco use has been substantially more common among women. The widespread pattern of greater tobacco use by men appears to be linked to general features of sex roles. For example, men have often had greater social power than women, and this has been expressed in greater restrictions on women's behavior, including social prohibitions against women's smoking. These social prohibitions against women's smoking have strongly inhibited women's tobacco use and thus have been a major cause of gender differences in tobacco use. Gender differences in tobacco use have varied in magnitude, depending on the type of tobacco use and the particular cultural group, age group and historical period considered. Causes of the variation in gender differences in tobacco use include variation in women's status and variation in the social significance and benefits attributed to particular types of tobacco use in different cultures. Contact with Western cultures appears to have increased or decreased gender differences in smoking, depending on the specific circumstances. The patterns of gender differences in tobacco use in non-Western societies are similar in many ways to the patterns observed in Western societies, but there are several important differences. For example, current evidence indicates that in many non-Western societies women have been more likely than men to quit smoking, in contrast to most Western societies where men have been as likely or more likely than women to quit smoking.smoking tobacco use gender sex differences cross-cultural