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Background and Aims: Management of alcohol use disorder (AUD) could be enhanced by effective remote treatments. This study tested whether supplementing intensive outpatient programs (IOPs) with continuing care delivered via (1) telephone, (2) smartphone or (3) their combination improves outcomes relative to (4) IOP only. Continuing care conditions were also compared. Design Randomized controlled trial of four groups with 3‐, 6‐, 9‐, 12‐ and 18‐month follow‐ups. Setting: University research center in Philadelphia, PA, USA. Participants: Participants (n = 262) met DSM‐V criteria for AUD, were largely male (71%) and African American (82%). Interventions and Comparator: Telephone monitoring and counseling (TMC; n = 59), addiction comprehensive health enhancement support system (ACHESS; n = 68) and TMC + ACHESS (n = 70) provided for 12 months. The control condition received IOP only (TAU; n = 65). Measurement The primary outcome was percentage of days heavy drinking (PDHD) in months 1–12. Secondary outcomes were any drinking, any drug use, drinking consequences and quality of life. Findings Mean PDHD in months 1–12 was 10.29 in TAU, 5.41 in TMC, 6.80 in ACHESS and 5.99 in TMC + ACHESS. PDHD was lower in TMC [Cohen's d = 0.35, P = 0.018, 95% confidence interval (CI) = (−1.42, −0.20)], ACHESS [d = 0.31, P = 0.031, 95% CI = (−1.27, −0.06)] and TMC + ACHESS [d = 0.36, P = 0.009, 95% CI = (−1.40, −0.20)] than in TAU. Differences between TMC + ACHESS, TMC and ACHESS were small (d ≤ 0.06) and non‐significant. Findings were inconclusive as to whether or not the treatment conditions differed on PDHD at 18 months. A significant effect was obtained on any drinking, which was higher in months 1–12 in TAU than in TMC [odds ratio (OR) = 3.02, standard error (SE) = 0.43, 95% CI = (1.30, 6.99), P = 0.01] and TMC + ACHESS [OR = 2.43, SE = 0.39, 95% CI = (1.12, 5.27), P = 0.025). No other significant effects were obtained on other secondary outcomes during or after treatment. Conclusions: A telephone‐delivered intervention and a smartphone‐delivered intervention, alone and in combination, provided effective remote continuing care for alcohol use disorder. The combination of both interventions was not superior to either alone and effects did not persist post‐treatment. [ABSTRACT FROM AUTHOR]

Objective: This investigation explores how using different e‐health interventions facilitates positive psychosocial changes and how these changes reduce cancer concerns and improve quality of life in breast cancer patients over time. Methods: A total of 326 breast cancer patients were randomly assigned to one of three e‐health interventions: (a) Internet only, (b) the Comprehensive Health Enhancement Support System information and support services (CHESS‐IS), or (c) CHESS with mentor. Proximal health outcomes such as information overload, emotional functioning, and social support were measured alongside distal outcomes like cancer concerns and quality of life. Participants completed surveys at four time points: pretest as a baseline, 6 weeks, 3 months, and 6 months. Results: Both interventions were effective in improving patient health beyond Internet only but they differed in type of change mechanism and clinical benefit. The CHESS‐IS enhanced proximal outcomes at 3 months through improved information competence. The CHESS with mentor intervention reduced breast cancer concerns at 6 months, mediated mainly by emotional‐social competence and emotional functioning. Conclusions: Using e‐health interventions like CHESS can help patients improve cancer information management skills and emotional functioning, contributing to better short‐term health outcomes. Adding a human mentor can enhance the benefits of CHESS use, extending the experience among breast cancer patients. Theoretical, practical, and clinical implications of the study results are discussed. [ABSTRACT FROM AUTHOR]

Background: Hospitals frequently evaluate their service quality based on the care and services provided to patients by their clinical and non‐clinical staff.1,2 However, such evaluations do not take into consideration the many interactions that patients have in online patient communities with the health‐care organization (HCO) as well as with peer patients. Patients' interactions in these online communities could impact their perceptions regarding the HCO's service quality. Purposes: The objective of this pilot study was to evaluate the impact of social support and responsiveness that patients experience in an HCO's online community on patients' perceptions regarding the HCO's service quality. Method: The study data are collected from CHESS, a health‐care programme (Comprehensive Health Enhancement Support System) run by the Centre for Health Enhancement System Studies at the University of Wisconsin–Madison. Findings: Findings show that the social support and the responsiveness received from peer patients in the online patient communities will impact patients' perceptions regarding the service quality of the HCO even when the organizational members themselves do not participate in the online discussions. Practice implications: The results indicate that interactions in such HCO‐provided online patient communities should not be ignored as they could translate into patients' perceptions regarding HCOs' service quality. Ways to improve responsiveness and social support in an HCO's online patient community are discussed. [ABSTRACT FROM AUTHOR]

Aims Improvement collaboratives consisting of various components are used throughout health care to improve quality, but no study has identified which components work best. This study tested the effectiveness of different components in addiction treatment services, hypothesizing that a combination of all components would be most effective. Design An unblinded cluster-randomized trial assigned clinics to one of four groups: interest circle calls (group teleconferences), clinic-level coaching, learning sessions (large face-to-face meetings) and a combination of all three. Interest circle calls functioned as a minimal intervention comparison group. Setting Out-patient addiction treatment clinics in the United States. Participants Two hundred and one clinics in five states. Measurements Clinic data managers submitted data on three primary outcomes: waiting-time (mean days between first contact and first treatment), retention (percentage of patients retained from first to fourth treatment session) and annual number of new patients. State and group costs were collected for a cost-effectiveness analysis. Findings Waiting-time declined significantly for three groups: coaching (an average of 4.6 days/clinic, P = 0.001), learning sessions (3.5 days/clinic, P = 0.012) and the combination (4.7 days/clinic, P = 0.001). The coaching and combination groups increased significantly the number of new patients (19.5%, P = 0.028; 8.9%, P = 0.029; respectively). Interest circle calls showed no significant effect on outcomes. None of the groups improved retention significantly. The estimated cost per clinic was $2878 for coaching versus $7930 for the combination. Coaching and the combination of collaborative components were about equally effective in achieving study aims, but coaching was substantially more cost-effective. Conclusions When trying to improve the effectiveness of addiction treatment services, clinic-level coaching appears to help improve waiting-time and number of new patients while other components of improvement collaboratives (interest circles calls and learning sessions) do not seem to add further value. [ABSTRACT FROM AUTHOR]

Background: In this study, the authors examined the effectiveness of an online support system (Comprehensive Health Enhancement Support System [CHESS]) versus the Internet in relieving physical symptom distress in patients with non-small cell lung cancer (NSCLC).Methods: In total, 285 informal caregiver-patient dyads were assigned randomly to receive, for up to 25 months, standard care plus training on and access to either use of the Internet and a list of Internet sites about lung cancer (the Internet arm) or CHESS (the CHESS arm). Caregivers agreed to use CHESS or the Internet and to complete bimonthly surveys; for patients, these tasks were optional. The primary endpoint-patient symptom distress-was measured by caregiver reports using a modified Edmonton Symptom Assessment Scale.Results: Caregivers in the CHESS arm consistently reported lower patient physical symptom distress than caregivers in the Internet arm. Significant differences were observed at 4 months (P = .031; Cohen d = .42) and at 6 months (P = .004; d = .61). Similar but marginally significant effects were observed at 2 months (P = .051; d = .39) and at 8 months (P = .061; d = .43). Exploratory analyses indicated that survival curves did not differ significantly between the arms (log-rank P = .172), although a survival difference in an exploratory subgroup analysis suggested an avenue for further study.Conclusions: The current results indicated that an online support system may reduce patient symptom distress. The effect on survival bears further investigation. [ABSTRACT FROM AUTHOR]

Objective: To better understand the process and effect of social support exchanges within computer-mediated social support (CMSS) groups for breast cancer patients, this study examines (1) the dynamic interplay between emotional support giving and receiving and (2) the relative effects of emotional support giving and receiving on patients' psychosocial health outcomes. Methods: Data was collected from 177 patients who participated in online cancer support groups within the Comprehensive Health Enhancement Support System (CHESS) during the 4-month intervention. Data included (1) pretest and/or post-test survey scores of demographic, disease-related, and psychosocial factors, (2) automatically collected CHESS usage data, and (3) computer-aided content analysis of social support messages posts. Results: Hierarchical regression analyses revealed that those who receive higher levels of support from others have fewer breast cancer-related concerns ( β = − 0.15, p<0.05), while those who give higher levels of support to others reframe their own problems in a positive light and adopt more positive strategies for coping ( β = 0.16, p<0.05). In addition to these positive effects, partial correlation analysis indicated that these two supportive behaviors are reciprocal. Conclusions: We concluded that supportive exchanges of receiving and giving play positive, but different, roles in predicting psychosocial health outcomes. Moreover, emotional support giving and receiving tend to reinforce each other. Our findings help practitioners, health-care providers, and health system designers make sense of diverse social support processes among cancer patients participating within CMSS groups. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We evaluated the helpfulness of informational, emotional, and decision-making support received by women newly diagnosed with breast cancer from their family, friends, and health care providers. Data were collected at two time points via patient surveys: baseline on an average 2 months post-diagnosis and follow-up at 5 months post-baseline. In the period closer to diagnosis, majority of the women received helpful informational support from health care providers (84.0%); helpful emotional support from family (85%), friends (80.4%), and providers (67.1%); and helpful decision-making support from providers (75.2%) and family (71.0%). Emotional support at baseline and emotional and informational support at 5-month follow-up were significantly associated with patients' health-related quality of life and self-efficacy outcomes (p<0.01). Perceived helpfulness of informational, emotional, and decision-making support provided by family, friends, and providers however significantly decreased over time (p<0.001). Cancer patients' desire significant amount of support throughout their cancer journey. Our results show that while patients receive a lot of support during the period closer to diagnosis, receipt of helpful support drops significantly within the first year itself. In order to facilitate cancer patients' adjustment to their illness, efforts need to be made to understand and address their support needs throughout the cancer experience. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective. To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources. Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods. A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection. For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results. Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions. A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. [ABSTRACT FROM AUTHOR]