To better define potential challenges in dental professional ethics, the authors gathered data regarding patients' characterizations of an ideal dentist and compared them with their impressions of ...dentists in general.
The authors invited 500 consecutively seen primary care patients at an academic medical center to participate in the study. Participants completed a 32-item survey assessing key domains of ethical characteristics of health care professionals: trustworthiness, honesty, beneficence, nonmaleficence, respect for autonomy, empathy, compassion, patience, courage, humility and dedication. The authors used the McNemar paired t test to compare respondents' ratings of ideal dentists with their ratings of dentists in general.
Two hundred eight-five patients returned completed surveys, for a response rate of 57 percent. The authors found statistically significant differences between ideal and perceived characteristics in all but one domain. The area of greatest difference related to the domain of trustworthiness (that is, dentists should not "propose unnecessary treatments just so they can make money"). For this survey item, 98 percent of patients reported that it was very or extremely important, but only 57 percent of respondents moderately or strongly agreed that dentists in general were engaging in this practice (P < .0001).
These data reveal gaps between patients' expectations of the dental profession and their actual impressions of dentists in general. Addressing these discrepancies may be crucial if dentistry is to continue to enjoy the public's trust.
Ongoing payment reform in dialysis necessitates better patient outcomes and lower costs. Suggested improvements to processes of care for maintenance dialysis patients are abundant; however, their ...impact on patient-important outcomes is unclear. This systematic review included comparative randomized controlled trials or observational studies with no restriction on language, published from 2000 to 2014, involving at least 5 adult dialysis patients who received a minimum of 6 months of follow-up. The effect size was pooled and stratified by intervention strategy (multidisciplinary care MDC, home dialysis, alternate dialysis settings, and electronic health record implementation). Heterogeneity (I
) was used to assess the variability in study effects related to study differences rather than chance. Of the 1988 articles screened, 25 international studies with 74,833 maintenance dialysis patients were included. Interventions with MDC or home dialysis were associated with a lower mortality (hazard ratio HR = 0.72, 95% confidence interval CI 0.61, 0.84, I
= 41.6%; HR = 0.57, 95% CI 0.41, 0.81, I
= 89.0%; respectively) and hospitalizations (incidence rate ratio IRR = 0.68, 95% CI 0.51, 0.91, I
= NA; IRR = 0.88, 95% CI 0.64, 1.20, I
= 79.6%; respectively). Alternate dialysis settings also were associated with a reduction in hospitalizations (IRR = 0.41, 95% CI 0.25, 0.69, I
= 0.0%). This systematic review underscores the importance of multidisciplinary care, and also the value of telemedicine as a means to increase access to providers and enhance outcomes for those dialyzing at home or in alternate settings, including those with limited access to nephrology expertise because of travel distance.
BACKGROUND
Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success.
OBJECTIVE
To identify components of ...a successful homebased CTP and patient characteristics that are most predictive of reduced 30‐day readmissions.
DESIGN
Retrospective cohort.
PATIENTS
A total of 315 community‐dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013.
SETTING
Midwest primary care practice in an integrated health system.
INTERVENTION
Enrollment in a CTP during acute hospitalization.
MEASUREMENTS
The primary outcome was all‐cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare.
RESULTS
The overall 30‐day readmission rate was 17.1%. The intensity of follow‐up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission.
CONCLUSIONS
No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.
Abstract Background Care transition programs can potentially reduce 30 day readmission; however, the effect on long-term hospital readmissions is still unclear. Objective We compared short-term (30 ...day) and long-term (180 day) utilization of participants enrolled in care transitions versus those matched referents eligible but not enrolled. Design This cohort study was conducted from January 1, 2011 until June 30, 2013 within a primary care academic practice. Participants Patients at high risk for hospital readmission based on age and comorbid health conditions had participated in care transitions group (cases) or usual care (referent). Main measures The primary outcomes were 30, 90, and 180 day hospital readmissions.. Secondary outcomes included: mortality; emergency room visits and days; combined rehospitalizations and emergency room visits; and total intensive care unit days. Cox proportional hazard models using propensity score matching were used to assess rehospitalization, emergency room visits and mortality. Poisson regression models were used to compare the numbers of hospital days. Key results Compared to referent ( n =365), Mayo Clinic Care Transitions patients exhibited a lower 30 day rehospitalization rate compared to referent; 12.4% (95% CI 8.9–15.7) versus 20.1% (95% CI 15.8–24.1%), respectively ( P =0.002). At 180-days, there was no difference in rehospitalization between transitions and referent; 39.9% (95% CI 34.6–44.9%) versus 44.8% (95% CI 39.4–49.8%), ( P =0.07). Conclusion We observed a reduction in 30 day rehospitalization rates among those enrolled in care transitions compared to referent. However, this effect was not sustained at 180 days. More work is needed to identify how the intervention can be sustained beyond 30 days.
Identifying patients at high risk of critical illness is necessary for the development and testing of strategies to prevent critical illness. The aim of this study was to determine the relationship ...between high elder risk assessment (ERA) score and critical illness requiring intensive care and to see if the ERA can be used as a prediction tool to identify elderly patients at the primary care visit who are at high risk of critical illness.
A population-based historical cohort study was conducted in elderly patients (age >65 years) identified at the time of primary care visit in Rochester, MN, USA. Predictors including age, previous hospital days, and comorbid health conditions were identified from routine administrative data available in the electronic medical record. The main outcome was critical illness, defined as sepsis, need for mechanical ventilation, or death within 2 years of initial visit. Patients with an ERA score of 16 were considered to be at high risk. The discrimination of the ERA score was assessed using area under the receiver operating characteristic curve.
Of the 13,457 eligible patients, 9,872 gave consent for medical record review and had full information on intensive care unit utilization. The mean age was 75.8 years (standard deviation ±7.6 years), and 58% were female, 94% were Caucasian, 62% were married, and 13% were living in nursing homes. In the overall group, 417 patients (4.2%) suffered from critical illness. In the 1,134 patients with ERA >16, 154 (14%) suffered from critical illness. An ERA score ≥16 predicted critical illness (odds ratio 6.35; 95% confidence interval 3.51-11.48). The area under the receiver operating characteristic curve was 0.75, which indicated good discrimination.
A simple model based on easily obtainable administrative data predicted critical illness in the next 2 years in elderly outpatients with up to 14% of the highest risk population suffering from critical illness. This model can facilitate efficient enrollment of patients into clinical programs such as care transition programs and studies aimed at the prevention of critical illness. It also can serve as a reminder to initiate advance care planning for high-risk elderly patients. External validation of this tool in different populations may enhance its generalizability.
We report a case of a healthy 24-year-old man who presented with acute renal failure and proteinuria while taking creatine and multiple other supplements for bodybuilding purposes. A renal biopsy ...showed acute interstitial nephritis. The patient recovered completely after he stopped taking the supplements. Creatine is a performance-enhancing substance that has gained widespread popularity among professional as well as amateur athletes. It is legal and considered relatively safe. Recently there have been case reports of renal dysfunction, including acute interstitial nephritis, associated with its use. Further studies are needed to evaluate the safety of creatine supplementation. It may be prudent to include a warning of this possible side effect in the product insert.
Frail multimorbid elders are at high risk for hospital readmission and can benefit from dedicated care transitions programs. Nonetheless, some patients remain at disproportionately heightened risk. ...In this retrospective study of 717 frail multimorbid community-dwelling elders (mean age = 83 years) enrolled in a care transitions program, we assessed the effect of specific medications and postdischarge medication changes on 30-day readmission. Patients treated with opioids, anticholinergic agents, or antihistamines and those requiring ≥ 4 medication changes after hospital discharge were at a significantly greater risk. This knowledge provides nurse practitioners an opportunity to individualize and improve the care of this vulnerable population.
•High-risk medications increase the risk of 30-day readmission in care transition elders.•Four or more medication changes after hospital discharge double the risk of 30-day readmission.•Opioid use after hospital discharge doubles the risk of 30-day readmission.•Anticholinergic/antihistamine use after discharge increases 30-day readmission risk.
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