Jennifer L. Pecina

A Randomized Controlled Trial of Telemonitoring in Older Adults With Multiple Health Issues to Prevent Hospitalizations and Emergency Department Visits

BACKGROUND Efficiently caring for frail older adults will become an increasingly important part of health care reform;telemonitoring within homes may be an answer to improve outcomes. This study sought to assess differences in hospitalizations and emergency department (ED) visits among older adults using telemonitoring vs usual care. METHODS A randomized controlled trial was performed among adults older than 60 years at high risk for rehospitalization. Participants were randomized to telemonitoring (with daily input) or to patient-driven usual care. Telemonitoring was accomplished by daily biometrics,symptom reporting, and videoconference. The primary outcome was a composite end point of hospitalizations and ED visits in the 12 months following enrollment. Secondary end points included hospitalizations,ED visits, and total hospital days. Intent-to-treat analysis was performed. RESULTS Two hundred five participants were enrolled,with a mean age of 80.3 years. The primary outcome of hospitalizations and ED visits did not differ between the telemonitoring group (63.7%) and the usual care group(57.3%) (P=.35). No differences were observed in secondary end points, including hospitalizations, ED visits,and total hospital days. No significant group differences in hospitalizations and ED visits were found between the pre-enrollment period vs the post-enrollment period. Mortality was higher in the telemonitoring group (14.7%)than in the usual care group (3.9%) (P=.008). CONCLUSIONS Among older patients, telemonitoring did not result in fewer hospitalizations or ED visits. Secondary outcomes demonstrated no significant differences between the telemonitoring group and the usual care group.The cause of greater mortality in the telemonitoring group is unknown.

A randomized controlled trial of telemonitoring in older adults with multiple chronic conditions: the Tele-ERA study

BackgroundOlder adults with multiple chronic illnesses are at risk for worsening functional and medical status and hospitalization. Home telemonitoring may help slow this decline. This protocol of a randomized controlled trial was designed to help determine the impact of home telemonitoring on hospitalization. The specific aim of the study reads as follows: to determine the effectiveness of home telemonitoring compared with usual care in reducing the combined outcomes of hospitalization and emergency department visits in an at-risk population 60 years of age or older.Methods/DesignTwo-hundred patients with the highest 10% Mayo Clinic Elder Risk Assessment scores will be randomly assigned to one of two interventions. Home telemonitoring involves the use of a computer device, the Intel Health Guide, which records biometric and symptom data from patients in their homes. This information is monitored by midlevel providers associated with a primary care medical practice. Under the usual care scenario, patients make appointments with their providers as problems arise and use ongoing support such as a 24-hour nurse line.Patients will have initial evaluations of gait and quality of life using instruments such as the SF-12 Health Survey, the Kokmen Short Test of Mental Status, and the PHQ-9 health questionnaire. Patients will be followed for 1 year for primary outcomes of hospitalizations and emergency department visits. Secondary analysis will include quality of life, compliance with the device, and attitudes about telemonitoring. Sample size is based on an 80% power to detect a 36% difference between the two groups. The primary analysis will involve Cox proportional time-to-event analysis. Secondary analysis will use t-test comparisons for continuous variables and the chi square test for proportional analysis.DiscussionPatients randomized to home telemonitoring will have daily assessments of their health status using the device. Registered nurse monitoring will assess any change in status followed by videoconferencing by a mid-level provider. We obtained trial registration and Institutional Review Board approval.Trial registrationTrial registration number through http://www.clinicaltrials.gov:%20NCT01056640.

The impact of telemonitoring upon hospice referral in the community: a randomized controlled trial

Background Using telemedicine for older adults with multiple comorbid conditions is a potential area for growth in health care. Given this older, ailing population, providers should discuss end-of-life care with patients. Objective To determine the relationship between telemonitoring and hospice enrollment compared to usual care among older adults with chronic health problems. Methods This was a secondary evaluation of a randomized controlled trial. The trial was performed at an academic medical center. Patients who were over the age of 60 and had a high risk of hospitalization and emergency department visits were recruited to the study. The primary outcome was hospice enrollment, and the secondary outcome was the mean number of days in hospice. The data were analyzed using Chi-squared tests and time-to-event analysis. Results The average age of the cohort was 80.3 years. Nine patients (9.6%) in the telemonitoring group were enrolled in hospice care, whereas four patients (4.0%) in the usual care group were enrolled (P = 0.12). The mean number of days in hospice was 57.9 (SD ± 99.2) for the telemonitoring group, and 119.3 (SD ± 123.8) for the usual care group (P = 0.36). There was no significant difference regarding time to hospice referral. Conclusion In this pilot analysis, there were no differences noted between groups in the number of patients that entered into hospice or the amount of time they stayed in hospice care. This was a small trial, and the power to detect a difference was 36%. It was encouraging that twice the number of patients enrolled in hospice care in the telemonitoring group compared to usual care despite the insignificant finding. Further research may determine the effect of telemonitoring upon hospice referral.

Comparison of Clinical Risk Tools for Predicting Osteoporosis in Women Ages 50–64

Objective: The objective of this study was to compare the performance of the US Preventive Services Task Force (USPSTF) recommended WHO Fracture Risk Assessment Tool (FRAX) threshold score of 9.3% (calculated without femoral neck bone density) with the Simple Calculated Osteoporosis Risk Estimate (SCORE), Osteoporosis Self-Assessment Tool (OST), and the Osteoporosis Risk Assessment Instrument (ORAI) to identify osteoporosis in younger women. Methods: We conducted a retrospective review of women ages 50 to 64 years who underwent dual-energy radiographic absorptiometry (DXA) at our institution over a 6-month period. Scores for the FRAX, ORAI, OST, and SCORE tools were calculated using various thresholds: FRAX ≥9.3%, SCORE ≥6, OST <2, and ORAI ≥9. Sensitivity, specificity, and area under the receiver-operating characteristic curve for detection of densitometric osteoporosis by DXA for each tool were compared. Results: A total of 290 women were identified. Of these, 284 (97.9%) were white, and the mean ± standard deviation age was 56.6 ± 3.4 years. Fifty (17.2%) had osteoporosis of the lumbar spine and/or femoral neck on DXA. Sensitivity, specificity, and area under the receiver-operating characteristic curve for identifying densitometric osteoporosis at the femoral neck and/or spine were 36%, 73%, and 0.55 for FRAX; 74%, 42%, and 0.58 for SCORE; 56%, 69%, and 0.63 for the OST; and 52%, 67%, and 0.60 for the ORAI, respectively. Conclusions: DXA screening based on the USPSTF–recommended FRAX threshold score of 9.3% has a low sensitivity to identify densitometric osteoporosis in women ages 50 to 64. Lowering the threshold score would increase sensitivity but would also increase the number of women sent for screening DXA. Use of the validated SCORE tool would improve sensitivity to identify osteoporosis in this age group.

Levothyroxine dosage is associated with stability of thyroid-stimulating hormone values.

OBJECTIVE In patients treated for hypothyroidism, the usual practice is to monitor thyroid-stimulating hormone values yearly once a therapeutic dosage of levothyroxine is determined. This study investigates whether there are any clinical predictors that could identify a subset of patients who might be monitored safely on a less frequent basis. METHODS With the use of a retrospective study design, 715 patients treated for hypothyroidism who had a normal (ie, therapeutic) thyroid-stimulating hormone value in 2006 while taking levothyroxine were identified. All thyroid-stimulating hormone values were then obtained through December 31, 2012. By using a Cox proportional hazard model, gender, age, body mass index, history of chronic autoimmune thyroiditis, initial thyroid-stimulating hormone level, and levothyroxine dose were analyzed for time to first abnormal thyroid-stimulating hormone value. RESULTS Age, gender, history of chronic autoimmune thyroiditis, and body mass index at the time of initial normal thyroid-stimulating hormone were not associated significantly with time to abnormal thyroid-stimulating hormone value. Levothyroxine dose >125 μg/day had an increased hazard ratio of 2.4 (95% confidence interval, 1.7-3.4; P < .0001) for time to first follow-up abnormal thyroid-stimulating hormone value, but dosages less than that did not increase the hazard ratio. One year after the initial normal thyroid-stimulating hormone value, 91.1% of patients taking ≤ 125 μg/day had a continued normal thyroid-stimulating hormone, whereas only 73.3% of patients taking >125 μg/day did. Transformed thyroid-stimulating hormone value (which represents a measure of how far the initial thyroid-stimulating hormone was from the midpoint of the normal range) also had an increased hazard ratio of 1.14 (95% confidence interval, 1.1-1.2; P < .0001) for time to first abnormal thyroid-stimulating hormone value. CONCLUSIONS For patients receiving ≤ 125 μg/day of levothyroxine, we propose that a testing interval up to 2 years may be acceptable if their thyroid-stimulating hormone is well within the normal range.

Nurse Practitioners and Physician Assistants Are Complements to Family Medicine Physicians

Controlling the overall cost of medical care requires controlling the number of physician visits. Nurse practitioners and physician assistants (NPs/PAs) may function as lower-cost substitutes for physicians or they may complement physician services. The association between NP/PA and physician visits when NPs/PAs are not working as primary care providers (PCPs) has not been thoroughly studied. A sample of 400 family medicine patients drawn from 1 large multisite practice was studied using multiple logistic regression analysis. NPs/PAs did not function as PCPs during the study period. Patients were defined as outliers if they visited physicians more than 5 times in a year. Patients who visited NPs/PAs in non-retail clinics were significantly more likely to be physician visit outliers. Visits to NPs/PAs in retail clinics were not related to physician visits. NP/PA visits in standard medical office settings complement physician visits when the NPs/PAs were not working as PCPs in this large multisite practice. Health care reform proposals relying on increased use of NPs/PAs may be more cost-efficient if NPs/PAs are located in retail settings or function as PCPs.

Early e-consultation face-to-face conversions:

Introduction E-consultations are asynchronous, text-based consultations. The specialist e-consultant answers clinical questions in a similar way to a standard consultation but the questions and answers are sent electronically. The e-consultant has access to some or all of the medical record but does not have contact with the patient. Although e-consultations are meant to substitute for face-to-face (F2F) consultations, a significant proportion of e-consultations are converted to F2F consultations. Methods We examined e-consultation content from a sample of e-consultations that had subsequent F2F visits in the same specialty as the e-consultation within 28 days of the e-consultation. Results Out of 5115 e-consultations, there were a total of 547 (10.7%) early F2F conversions. One hundred and fifty-one e-consultations with subsequent early F2F conversions were reviewed in eight specialties. In 64% of the F2F conversions, specialists recommended the F2F consultations. In 75% there were complex diagnostic or treatment considerations. In only 1% was there a sense of medical urgency or a stated need for physical examination. Discussion E-consultations convert to F2F consultations primarily at the request of the specialist. Diagnostic and treatment complexity appear to be the main reasons. We found little evidence that patients decided independently to get a F2F visit or that specialists needed a F2F visit to perform a physical examination. Although e-consultations might not be a complete substitute for F2F consultations, they may serve as an entry level consultation that could be supplemented by a video consultation as needed for cases with more diagnostic and treatment complexity.

Comparing performance of 30-day readmission risk classifiers among hospitalized primary care patients

RATIONALE, AIMS AND OBJECTIVES Hospital readmission within 30 days of discharge occurs in almost 20% of US Medicare patients and may be a marker of poor quality inpatient care, ineffective hospital to home transitions, or disease severity. Within a patient centered medical home, care transition interventions may only be practical from cost and staffing perspectives if targeted at patients with the greatest risk of readmission. Various scoring algorithms attempt to predict patients at risk for 30-day readmission, but head-to-head comparison of performance is lacking. Compare published scoring algorithms which use generally available electronic medical record data on the same set of hospitalized primary care patients. METHODS The LACE index, the LACE+ index, the HOSPITAL score, and the readmission risk score were computed on a consecutive cohort of 26,278 hospital admissions. Classifier performance was assessed by plotting receiver operating characteristic curves comparing the computed score with the actual outcome of death or readmission within 30 days. Statistical significance of differences in performance was assessed using bootstrapping techniques. RESULTS Correct readmission classification on this cohort was moderate with the following c-statistics: Readmission risk score 0.666; LACE 0.680; LACE+ 0.662; and HOSPITAL 0.675. There was no statistically significant difference in performance between classifiers. CONCLUSIONS Logistic regression based classifiers yield only moderate performance when utilized to predict 30-day readmissions. The task is difficult due to the variety of underlying causes for readmission, nonlinearity, and the arbitrary time period of concern. More sophisticated classification techniques may be necessary to increase performance and allow patient centered medical homes to effectively focus efforts to reduce readmissions.

Visit Entropy Associated with Hospital Readmission Rates

Purpose: The chronic disease model suggests continuity of care and team-based care can improve outcomes for multimorbidity patients and reduce hospitalizations. Continuity of care following admission has had mixed effects on readmission rates; however, its effect before admission has not been well studied. Increased outpatient care organization and continuity before admission is hypothesized to reduce the odds of readmission. Methods: In a cohort of 14,662 primary care patients from a Patient-Centered Medical Home (PCMH) practice, continuity of care in the 12 months before admission was assessed using 3 established metrics; usual provider continuity (UPC), dispersion continuity of care (COC), and sequence continuity (SECON). In addition, because these established metrics may not accurately reflect continuity in planned team-based care, a new metric called visit entropy (VE) was used to quantify the disorganization of visits. Multivariate logistic regression was performed to examine the relationship between readmission within 30 days and continuity while controlling for known readmission risk factors abstracted from an electronic medical record. Results: Higher VE was associated with readmission (odds ratio, 1.10; 95% confidence interval, 1.02 to 1.19). The continuity measures of UPC, COC, and SECON were not associated with readmission. Conclusions: Disorganized medical care, characterized by a higher VE, is associated with higher odds of readmission among hospitalized primary care patients. An association between traditional measures of continuity (UPC, COC, and SECON) and readmission was not found.

A retrospective analysis of medical record use in e-consultations.

Introduction Under certain circumstances, e-consultations can substitute for a face-to-face consultation. A basic requirement for a successful e-consultation is that the e-consultant has access to important medical history and exam findings along with laboratory and imaging results. Knowing just what information the specialist needs to complete an e-consultation is a major challenge. This paper examines differences between specialties in their need for past information from laboratory, imaging and clinical notes. Methods This is a retrospective study of patients who had an internal e-consultation performed at an academic medical centre. We reviewed a random sample of e-consultations that occurred in the first half of 2013 for the indication for the e-consultation and whether the e-consultant reviewed data in the medical record that was older than one year to perform the e-consultation. Results Out of 3008 total e-consultations we reviewed 360 (12%) randomly selected e-consultations from 12 specialties. Questions on management (35.8%), image results (27.2%) and laboratory results (25%) were the three most common indications for e-consultation. E-consultants reviewed medical records in existence more than one year prior to the e-consultation 146 (40.6%) of the time with e-consultants in the specialties of endocrinology, haematology and rheumatology, reviewing records older than one year more than half the time. Labs (20.3%), office notes (20%) and imaging (17.8%) were the types of medical data older than one year that were reviewed the most frequently overall. Discussion Management questions appear to be the most common reason for e-consultation. E-consultants frequently reviewed historical medical data that is older than one year at the time of the e-consultation, especially in endocrinology, haematology and rheumatology specialties. Practices engaging in e-consultations that require transfer of data may want to include longer time frames of historical information for those specialties.