Effect of an Online Weight Management Program Integrated With Population Health Management on Weight Change: A Randomized Clinical Trial.

Importance: Online programs may help with weight loss but have not been widely implemented in routine primary care. Objective: To compare the effectiveness of a combined intervention, including an online weight management program plus population health management, with the online program only and with usual care. Design, Setting, and Participants: Cluster randomized trial with enrollment from July 19, 2016, through August 10, 2017, at 15 primary care practices in the US. Eligible participants had a scheduled primary care visit and were aged 20 to 70 years, had a body mass index between 27 and less than 40, and had a diagnosis of hypertension or type 2 diabetes. Follow-up ended on May 8, 2019. Interventions: Participants in the usual care group (n = 326) were mailed general information about weight management. Participants in the online program only group (n = 216) and the combined intervention group (n = 298) were registered for the online program. The participants in the combined intervention group also received weight-related population health management, which included additional support from nonclinical staff who monitored their progress in the online program and conducted periodic outreach. Main Outcomes and Measures: The primary outcome was weight change at 12 months based on measured weights recorded in the electronic health record. Weight change at 18 months was a secondary outcome. Results: Among the 840 participants who enrolled (mean age, 59.3 years [SD, 8.6 years]; 60% female; 76.8% White), 732 (87.1%) had a recorded weight at 12 months and the missing weights for the remaining participants were imputed. There was a significant difference in weight change at 12 months by group with a mean weight change of -1.2 kg (95% CI, -2.1 to -0.3 kg) in the usual care group, -1.9 kg (95% CI, -2.6 to -1.1 kg) in the online program only group, and -3.1 kg (95% CI, -3.7 to -2.5 kg) in the combined intervention group (P < .001). The difference in weight change between the combined intervention group and the usual care group was -1.9 kg (97.5% CI, -2.9 to -0.9 kg; P < .001) and the difference between the combined intervention group and the online program only group was -1.2 kg (95% CI, -2.2 to -0.3 kg; P = .01). At 18 months, the mean weight change was -1.9 kg (95% CI, -2.8 to -1.0 kg) in the usual care group, -1.1 kg (95% CI, -2.0 to -0.3 kg) in the online program only group, and -2.8 kg (95% CI, -3.5 to -2.0 kg) in the combined intervention group (P < .001). Conclusions and Relevance: Among primary care patients with overweight or obesity and hypertension or type 2 diabetes, combining population health management with an online program resulted in a small but statistically significant greater weight loss at 12 months compared with usual care or the online program only. Further research is needed to understand the generalizability, scalability, and durability of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT02656693.

Primary Care Patients' and Providers' Perspectives about an Online Weight Management Program: a Qualitative Study.

BACKGROUND: Primary care providers (PCPs) often take the lead role in caring for patients with overweight and obesity; however, few PCPs counsel patients about weight loss. Online weight management programs that are integrated within primary care may help address this gap in care. OBJECTIVE: To identify perceptions of and experience with online weight management programs in general and with a proposed online program, to identify barriers to use, and to improve the design and content of our intervention, which included an online program plus population health management (PHM) support from primary care practices. DESIGN: A mixed qualitative methods study including three patient focus groups and seven semi-structured interviews with healthcare providers. PARTICIPANTS: A total of 13 adult patients (age range, 20-70) with body mass index (BMI) 27-35 kg/m2 attended the focus groups. In-person semi-structured interviews were conducted with seven healthcare providers (three PCPs, two population health managers, one primary care nurse, and one registered dietitian). MAIN MEASURES: We developed and used semi-structured focus groups and interview guides. The focus group and interviews were recorded and transcribed. Using grounded theory, we analyzed the transcripts to identify and extract common concepts and themes. KEY RESULTS: Although patients and healthcare providers expressed positive opinions about online weight management programs, few patients had experience with them, and providers stated that such programs are not being widely implemented in primary care settings. Some participants highlighted the flexibility and low cost as strengths of online weight management tools compared with in-person programs. All participants had favorable opinions about our proposed intervention and were overwhelmingly positive about the combination of an online program and PHM support. CONCLUSIONS: This study highlights the potential value of online weight management programs and PHM support in primary care. CLINICAL TRIALS REGISTRATION: NCT02656693.

The Impact of Automated Notification on Follow-up of Actionable Tests Pending at Discharge: a Cluster-Randomized Controlled Trial.

BACKGROUND: Follow-up of tests pending at discharge (TPADs) is poor. We previously demonstrated a twofold increase in awareness of any TPAD by attendings and primary care physicians (PCPs) using an automated email intervention OBJECTIVE: To determine whether automated notification improves documented follow-up for actionable TPADs DESIGN: Cluster-randomized controlled trial SUBJECTS: Attendings and PCPs caring for adult patients discharged from general medicine and cardiology services with at least one actionable TPAD between June 2011 and May 2012 INTERVENTION: An automated system that notifies discharging attendings and network PCPs of finalized TPADs by email MAIN MEASURES: The primary outcome was the proportion of actionable TPADs with documented action determined by independent physician review of the electronic health record (EHR). Secondary outcomes included documented acknowledgment, 30-day readmissions, and adjusted median days to documented follow-up. KEY RESULTS: Of the 3378 TPADs sampled, 253 (7.5%) were determined to be actionable by physician review. Of these, 150 (123 patients discharged by 53 attendings) and 103 (90 patients discharged by 44 attendings) were assigned to intervention and usual care groups, respectively, and underwent chart review. The proportion of actionable TPADs with documented action was 60.7 vs. 56.3% (p = 0.82) in the intervention vs. usual care groups, similar for documented acknowledgment. The proportion of patients with actionable TPADs readmitted within 30 days was 22.8 vs. 31.1% in the intervention vs. usual care groups (p = 0.24). The adjusted median days [95% CI] to documented action was 9 [6.2, 11.8] vs. 14 [10.2, 17.8] (p = 0.04) in the intervention vs. usual care groups, similar for documented acknowledgment. In sub-group analysis, the intervention had greater impact on documented action for patients with network PCPs compared with usual care (70 vs. 50%, p = 0.03). CONCLUSIONS: Automated notification of actionable TPADs shortened time to action but did not significantly improve documented follow-up, except for network-affiliated patients. The high proportion of actionable TPADs without any documented follow-up (~ 40%) represents an ongoing safety concern. CLINICAL TRIALS IDENTIFIER: NCT01153451.

Primary care patients' and providers' perspectives about an online weight management program integrated with population health management: Post-intervention qualitative results from the PROPS study.

Objective: To assess patients' and providers' attitudes about the online weight management program and population health management approach in the PROPS Study, which examined the effectiveness of these strategies in primary care. Methods: We conducted semi-structured interviews with 22 patients and nine providers. Using thematic analysis, we analyzed transcripts of the interviews to identify key themes. Results: Most patients found the online program to be well-structured and easy to use, although a few noted that the information was overwhelming or could be more personalized. Patients mentioned that the support from the population health managers was critical for their success, and several reported that they would have liked more involvement from their primary care provider or a dietitian. Providers also were satisfied with the interventions, and several stated that the population health management support was helpful because it added accountability. Providers suggested that the interventions could be improved by tailoring the information and integrating the online program with the electronic health record. Conclusion: Most patients and providers were satisfied with the interventions, with several recommendations for improvements. Innovation: These findings give additional information about patients' and providers' experience with this innovative approach for managing overweight and obesity in primary care.

Effects of an Intensive Discharge Intervention on Medication Adherence, Glycemic Control, and Readmission Rates in Patients With Type 2 Diabetes.

OBJECTIVES: Patients with diabetes are at particularly high risk for adverse outcomes after hospitalization. The goals of this study were to design, implement, and evaluate a multipronged transitional care intervention among hospitalized patients with diabetes. METHODS: We randomly assigned inpatients likely to be discharged home on insulin to an intensive transitional care intervention or usual care. The primary outcome was 90-day postdischarge insulin adherence, using prescription refill information to calculate a medication possession ratio. Unadjusted analyses were conducted using Wilcoxon rank sum; adjusted analyses used multivariable linear regression and weighted propensity scoring methods, with general estimating equations to account for clustering by admitting physician. RESULTS: One hundred eighty patients participated. The mean (SD) medication possession ratio to all insulin types was 84.5% (22.6) among intervention and 76.4% (25.1) among usual care patients (difference = 8.1, 95% confidence interval = -1.0 to 17.2, P = 0.06), with a smaller difference for adherence to all medications (86.3% versus 82.0%). A1c levels decreased in both groups but was larger in the intervention arm (1.09 and 0.11, respectively) (difference = -0.98, 95% confidence interval = -2.03 to -0.07, P = 0.04). Differences between study arms were not significant for rates of hypoglycemic episodes, 30-day readmissions, or emergency department visits. In adjusted/clustered analyses, the difference in A1c reduction remained statistically significant, whereas differences in all other outcomes remained nonsignificant. CONCLUSIONS: The intervention was associated with improvements in glycemic control, with nonsignificant trends toward greater medication adherence. Further research is needed to optimize and successfully implement interventions to improve patient safety and health outcomes during care transitions.

Integrating an online weight management program with population health management in primary care: Design, methods, and baseline data from the PROPS randomized controlled trial (Partnerships for Reducing Overweight and Obesity with Patient-centered Strategies).

BACKGROUND: Scalable, low-cost weight management strategies are needed in primary care. We conducted a pragmatic, cluster-randomized controlled trial to examine the effectiveness of an online weight management program integrated with population health management support. METHODS: We adapted an online weight management program and integrated it with population health management support in 15 primary care practices (24 clinics). We randomized the 24 clinics to usual care (UC), online program alone (OP), or combined intervention (CI). Eligible participants had to be ages 20 to 70 and have a recent primary care visit, body mass index (BMI) ≥ 27 and < 40 kg/m2, and a diagnosis of hypertension or type 2 diabetes. Participants attended routine visits and completed surveys over 18 months. The primary outcome is absolute weight change at 12 months (± 90 days) after enrollment, calculated from weights measured at primary care visits and recorded in the electronic health record. RESULTS: We enrolled 840 participants between July 2016 and August 2017 (326 UC, 216 OP, and 298 CI.) At enrollment, participants' mean age was 59.3 years, their mean weight was 203.1 pounds, and their mean BMI was 32.5 kg/m2; 60% of participants were female, 76.8% were white, 96.4% had hypertension, and 24.4% had type 2 diabetes. CONCLUSION: It is feasible to adapt an online weight management program and integrate it with population health management support in primary care. The results of this trial will provide valuable information about the effectiveness of these strategies in primary care settings. ClinicalTrials.govregistration number:NCT02656693.

An On-Treatment Analysis of the MARQUIS Study: Interventions to Improve Inpatient Medication Reconciliation.

It is unclear which medication reconciliation interventions are most effective at reducing inpatient medication discrepancies. Five United States hospitals' interdisciplinary quality improvement (QI) teams were virtually mentored by QI-trained physicians. Sites implemented one to seven evidence-based interventions in 791 patients during the 25-month implementation period. Three interventions were associated with significant decreases in potentially harmful discrepancy rates: (1) defining clinical roles and responsibilities, (2) training, and (3) hiring staff to perform discharge medication reconciliation. Two interventions were associated with significant increases in potentially harmful discrepancy rates: training staff to take medication histories and implementing a new electronic health record (EHR). Hospitals should focus first on hiring and training pharmacy staff to assist with medication reconciliation at discharge and delineating roles and responsibilities of clinical staff. We caution hospitals implementing a large vendor EHR, as medication discrepancies may increase. Finally, the effect of medication history training on discrepancies needs further study.

Effects of a multifaceted medication reconciliation quality improvement intervention on patient safety: final results of the MARQUIS study.

BACKGROUND: Unintentional discrepancies across care settings are a common form of medication error and can contribute to patient harm. Medication reconciliation can reduce discrepancies; however, effective implementation in real-world settings is challenging. METHODS: We conducted a pragmatic quality improvement (QI) study at five US hospitals, two of which included concurrent controls. The intervention consisted of local implementation of medication reconciliation best practices, utilising an evidence-based toolkit with 11 intervention components. Trained QI mentors conducted monthly site phone calls and two site visits during the intervention, which lasted from December 2011 through June 2014. The primary outcome was number of potentially harmful unintentional medication discrepancies per patient; secondary outcome was total discrepancies regardless of potential for harm. Time series analysis used multivariable Poisson regression. RESULTS: Across five sites, 1648 patients were sampled: 613 during baseline and 1035 during the implementation period. Overall, potentially harmful discrepancies did not decrease over time beyond baseline temporal trends, adjusted incidence rate ratio (IRR) 0.97 per month (95% CI 0.86 to 1.08), p=0.53. The intervention was associated with a reduction in total medication discrepancies, IRR 0.92 per month (95% CI 0.87 to 0.97), p=0.002. Of the four sites that implemented interventions, three had reductions in potentially harmful discrepancies. The fourth site, which implemented interventions and installed a new electronic health record (EHR), saw an increase in discrepancies, as did the fifth site, which did not implement any interventions but also installed a new EHR. CONCLUSIONS: Mentored implementation of a multifaceted medication reconciliation QI initiative was associated with a reduction in total, but not potentially harmful, medication discrepancies. The effect of EHR implementation on medication discrepancies warrants further study. TRIAL REGISTRATION NUMBER: NCT01337063.

The prevalence of personality disorders in hypochondriasis.

BACKGROUND: Although Axis I hypochondriasis is closely related to certain personality characteristics, the nature and extent of personality dysfunction in these patients still needs clarification. This study assessed the prevalence of personality disorders observed in hypochondriacal patients, described the types and comorbidity of personality disorders, and compared the psychological distress of patients with and without the most common comorbid personality disorder. METHOD: One hundred fifteen patients meeting Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria for hypochondriasis completed self-administered assessments, including the Personality Diagnostic Questionnaire-4+ (PDQ-4+), the Hopkins Symptom Checklist-90-Revised (SCL-90-R), the Whiteley Index, and the Somatic Symptom Inventory. These data were taken from a study conducted between September 1997 and November 2001. RESULTS: Eighty-eight patients (76.5%) had 1 or more personality disorders, whereas 27 patients (23.5%) had no personality disorders. Fifty-one patients (44.3%) had more than 3 personality disorders. The most common personality disorder in the hypochondriacal patients was obsessive-compulsive personality disorder (OCPD; 55.7%), followed by avoidant personality disorder (40.9%). The comorbidity of OCPD and avoidant personality disorder was 53.1% (34 of 64 patients with OCPD). The total PDQ-4+ score of the 64 patients with OCPD was significantly higher than that of the 51 patients without OCPD. On the SCL-90-R, the 64 patients with OCPD showed significantly higher scores on all of 3 global indices and 7 of 10 primary symptom dimensions (paranoid ideation, depression, anxiety, phobic anxiety, obsessive-compulsive, interpersonal sensitivity, and psychoticism) on the SCL-90-R compared to the 51 patients without OCPD. CONCLUSIONS: The high prevalence of personality disorders, particularly OCPD, among patients with hypochondriasis suggests that consideration of personality features is important in assessment and therapeutic interventions for hypochondriasis.