WellNet program and study design
Sonic Clinical Services (SCS) designed a 12-month chronic disease management (CDM) program called ‘WellNet’ which aims to provide a GP-led, MDT based care for patients with one or more chronic conditions. This enhanced primary care program is built upon the principles of PCMH and guided by evidence based, best practice models of clinical care to deliver high quality patient-centric care that is configured to individual risk and complexity levels .
Patients were recruited between December 2016 and October 2017 using a targeted convenience sampling technique. Targeted convenience sampling is a commonly used non-probability sampling in clinical research where members of the target population that meet certain practical eligibility criteria are included for the purpose of the study . We used a before-and-after case-series study design to evaluate the outcome of WellNet program in improving patient activation levels among primary care patients enrolled in six general practices in Northern Sydney, Australia. A written informed consent was obtained from the participants who enrolled in the 12-month study. A detailed description of the program design and evaluation are reported elsewhere .
The Participant, Intervention, Comparator, and Outcome statement (PICO) is briefly summarised in Box 1. Potentially eligible patients (N=1790) were contacted either through letter invites (N=1431) or GP referrals (N=359) for initial assessment. Eligibility criteria included patients aged 40 years and above; having one or more chronic condition/s; who had consulted a GP three times in the previous two years; and had a Hospital Admission Risk Profile (HARP) score of more than 10. The HARP risk assessment tool determines the likelihood of people with chronic or complex care needs presenting to hospital for treatment in the following 12 months . In addition, patients with one or more consistently elevated clinical risk factors were also invited to participate through GP referrals. Further details of the patient algorithm, recruitment outcomes, and data collection are reported elsewhere .
Of the 1790 contacted patients, 698 attended the initial assessment for eligibility and 10 patients were deemed ineligible to participate in the program due to reasons of living in nursing homes and diagnosis of severe cognitive impairment or terminal illness. Out of eligible 688 patients, 52 declined to participate in the program due to unknown reasons resulting in 636 patients. Of the 636 consenting participants, 626 reported their baseline PAM score and were included in this study. The flowchart of patient recruitment outcomes is shown in Figure 1. The WellNet study includes a well-matched comparison group based on age, gender, type and number of chronic conditions. However, self-reported health assessments such as PAM assessments were recorded only among the treatment group, therefore, limiting analyses to within-group rather than between-group comparison with standard primary care (comparison group)
The 12-month WellNet program is designed to integrate GPs with specially trained chronic disease management (CDM) Care Coordinators (CC) within each of the six participating GP practices . On entry to the program, the team of GPs and CCs coordinate with patients in undertaking a range of validated general and disease-specific risk assessments to determine patient’s baseline health status and wellbeing. The information gathered from these assessments is then used to formulate an individualised CDM plan in consultation with the patient. Included in the care plan are patient driven health goals; modifying and training core skills to self-manage symptoms and medications; improving diet and physical activity; and reducing smoking and alcohol consumption . The care plan includes, and is shared with, all relevant members of the care team. Ongoing support to increase knowledge, understanding and maintenance of positive behaviour change; monitoring of progress towards health goals; and assistance to access health and social care are provided through a combination of in-practice and telephone contacts. A sample goal chart with timelines used by the care team to provide tailored care according to individual patient risk and complexity of disease with an example of patients diagnosed with type 2 diabetes, hypertension and depression is shown in Figure 2.
Furthermore, ongoing patient support are also supplemented through user-friendly online platform and a mobile application. “GoShare” is an online web-based tool that enables digital sharing of evidence-based patient-relevant education materials. Patients’ access to the materials and understanding are regularly monitored and assessed through self-reported surveys. The CCs focus on low adherence to usage or understanding, so that issues can be resolved. Furthermore, patients are also supported with a mobile application, called ‘MediTracker’, which links directly to the clinical records held at the practice, providing access to information such as current medications, pathology results, diagnoses and immunisation status . This is intended to encourage and empower patients to play an active role in their chronic disease care management.
Patient Activation Measure 13 item version
Patient activation was measured with the use of validated PAM-13 item version developed by Hibbard et al . PAM-13 is a self-reported questionnaire composed of 13 items relating to patients’ beliefs about healthcare, knowledge about their health condition, and confidence in managing health related tasks. Each item has five response options from 0 to 4 such as: (0) ‘not applicable’; (1) ‘strongly disagree’; (2) ‘disagree’; (3) ‘agree’; and (4) ‘strongly agree’. The raw responses range from 13-52 which are then transformed through Insignia’s proprietary natural logarithm to a standardized metric ranging from 0 to 100 (0 = lower activation; 100 = highest activation). The scores are classified into four levels of activation of Level 1 (≤47.0) – not believing activation is important; Level 2 (47.1 - 55.1) – Lacking knowledge or confidence in self-management of health; Level 3 (55.2 – 67) – Beginning to take action; and Level 4 (≥67.1) – Taking action but require support in maintaining positive behaviour change. Each of these levels provide insights into a range of health-related characteristics, including behaviours and outcomes . In addition, determining baseline scores allow MDT to determine the best approach to engage and educate patients and thus improve self-management behaviour. Studies reporting on validation of PAM scores indicate that the minimal clinically important difference (MCID) is at least a 4-point difference in PAM score in addition to transitioning from lower to higher PAM levels [20, 21]. MCID refers to the smallest change in an outcome score that is considered “important” or “worthwhile” by the practitioner and/or resulting in a change in patient management . Changes in outcomes exceeding this minimal threshold are considered clinically relevant .
PAM scores were recorded from the patients at the start and completion (12 months) of the WellNet program. Aligned to the outcome of patient activation, key demographic information of age, gender, type and number of chronic conditions, private health insurance status, and total program contacts were analysed in this study.
Self-management impact and readiness to change scale of HARP assessment
Question 6 of HARP reports on the self-management and readiness to change behaviours which includes several categories: No capacity for self-management; pre-contemplation (not ready for change) and contemplation (considering but unlikely to change); preparation (intending to take action in the immediate future); action (actively changing health behaviours) and maintenance (maintained behaviour for ≥6 months); and relapse. This scale was only used as a supplement to PAM assessment.
The primary outcome of interest for this study was changes in the mean PAM score between baseline and 12-months after controlling for potential baseline covariates such as age, gender, type and number chronic disease diagnosis, insurance status, median visits, and baseline PAM score. Secondary outcomes include: 1) changes in proportion of patients with respect to different levels of PAM and HARP’s self-management impact scale at follow-up; 2) association between PAM levels and self-management impact and readiness to change scale of the HARP risk assessment tool; 3) significant predictors of PAM scores at follow-up.
Descriptive statistics for continuous variables using mean and standard deviation (SD) and percentages for categorical measures are presented in Table 1. One-way analysis of variance (ANOVA) was conducted to test for significant between-group differences in means between
levels of patient activation corresponding to each variable at baseline. Pearson’s correlation coefficient test was also used to determine the association within and between PAM scores and HARP’s self-management impact scale at baseline and follow-up. Additionally, t-tests and chi-square test were performed to determine any significant difference between patients who completed the program and those who withdrew from the program.
Primary analysis included only those who reported both baseline and follow-up scores. Adjusted mean difference between baseline and follow-up was measured using repeated measures ANCOVA to control for baseline potential covariates such as age, gender, type and number chronic disease diagnosis, insurance status, and median visits. A sensitivity analysis was also conducted to evaluate adjusted differences in PAM scores between baseline and follow-up among patients with two or more chronic conditions.
Backward stepwise multivariate regression models were conducted to determine the predictors of PAM scores at 12-month follow-up. Independent baseline covariates tested against follow-up PAM scores in the univariate analysis included: age, gender, type and number chronic disease diagnosis, insurance status, median visits, and baseline PAM score. Any variable with p-value of <0.2 was then included in the multivariate model. Backward stepwise regression approach was used to reduce and create a final model while simultaneously assessing the fitness of model in order to avoid dropping of non-significant variables that may affect the model fitness. The final model constitutes variables, which when excluded, cause a prominent deviance change (p < 0.05) as compared to the corresponding X2 test statistic on the relevant degrees of freedom.
Internal consistency of pre and post PAM-13 items in this study were evaluated using Cronbach's alpha. R and SPSS (version 25) statistical software were used to conduct all the analyses. Significance level was set as 0.05 and all statistical tests were two-sided.