Seen through the patients’ eyes: quality of chronic illness care

Seen through the patients’ eyes: quality of chronic illness care Abstract Background Most well-developed healthcare systems are facing the challenge of managing the increasing prevalence of patients with chronic diseases. Comprehensive frameworks, such as the chronic care model (CCM), receive widespread acceptance for improving care processes, clinical outcomes and costs. Objective The purpose of this study was to explore chronic patients’ perceptions of the quality of chronic care and the alignment with the CCM. Since previous research indicated that a patient’s assessment may depend on socio-demographic or disease-related characteristics, the relationship between the mean Patient Assessment of Chronic Illness Care (PACIC) score and possible aforementioned predictors was also explored. Methods An observational, cross-sectional study design was applied, and participants were recruited from the Flemish Patients’ Platform (Belgium). An online questionnaire was designed to assess chronic patients’ socio-demographic characteristics, medical consumption, quality of life (EuroQol-5D survey) and the perspective of chronic illness care PACIC survey. Results The mean overall PACIC score was 2.87 on a maximum score of 5. The highest mean score for the PACIC subscales was found for ‘patient activation’ (3.26), followed by ‘delivery system design/decision support’ (3.23), ‘problem solving/contextual counselling’ (2.86), ‘goal setting/tailoring’ (2.70) and ‘follow-up/coordination’ (2.59). Quality of life, as measured by the EuroQol Visual Analogue Scale, had a significantly positive correlation with the mean PACIC score (P = 0.005). Conclusion The CCM is considered an important step towards improved care for patients with chronic diseases. However, the findings of this study showed that elements from the CCM have not yet been fully implemented. Aspects such as dealing with problems which interfered with achieving predefined goals, helping patients to set specific goals for their care delivery and arranging follow-ups are less common in today’s care of chronic diseases. Chronic care, patient preference, quality improvement, quality of care, quality of life Introduction Tremendous progress has been made in healthcare which resulted in large falls in death rates for many life-threatening conditions such as HIV/AIDS, heart attacks and strokes (1). As a result, life expectancy at birth around the globe increased (1). According to the latest study in The Lancet, the Global Burden of Disease Study published in 2015, the world population has gained more than 10 years of life expectancy since 1980 (rising to 69.0 years for men and 74.8 years for women in 2015) (1). The progress in healthcare is worthy of praise, but the future sustainability of healthcare systems is nevertheless jeopardized. Although overall life expectancy has increased by 10.1 years between 1980 and 2015, healthy life expectancy has increased steadily by 6.1 years, which results in more years of life with illness and disability and a consequential high burden on individuals’ quality of life (1). Current delivery of care is, however, often fragmented and largely built around the long-standing acute and episodic model of care, although solid evidence shows that a more integrated and proactive approach helps to reduce the burden of many chronic diseases (2). Comprehensive frameworks, such as the chronic care model (CCM), increasingly receive widespread acceptance for improving care processes, clinical outcomes and healthcare-associated costs (3). The CCM is an evidence-based framework to guide chronic care delivery that supports patient self-management. The framework is structured around integrated healthcare teams and incorporates clinical information systems to facilitate productive patient–professional relationships and to enhance chronic care (4). The CCM describes six elements of a healthcare system that collaboratively encourage high-quality chronic care delivery: (i) organization of healthcare, (ii) clinical information systems, (iii) delivery system design, (iv) decision support, (v) self-management support and (vi) community linkages (4). Implementation of the CCM has been found to improve patient outcomes and reduce healthcare costs (5,6). There is growing consensus that patients can play a more active role in improving healthcare as they increasingly recognize the defects in their own care (7). The Patient Assessment of Chronic Illness Care (PACIC) survey is designed to assess patients’ experiences regarding continuity of chronic care delivery (8). The survey is proven to be an effective instrument to measure the alignment of chronic care with the CCM, that is measuring care that is patient-centred, proactive, planned and includes collaborative goal setting, problem-solving and follow-up support (9). Objectives The aim of the present study was to assess the quality of current chronic care delivery among patients living in Belgium (Flanders). Since previous research indicated that a patient’s assessment may depend on socio-demographic or disease-related characteristics, the relationship between the mean PACIC score and possible aforementioned predictors was also explored (10). Methods The current study is part of CORTEXS (Care Organisation: a Re-Thinking EXpedition in search for Sustainability), an extensive multidisciplinary research project in Flanders (Belgium), that studies integrated care from the micro-level of care recipients and their caregivers, over the meso-level of intra- and inter-organizational processes, to the macro-level of legal and financial frameworks (11). Design and recruitment An observational, cross-sectional study design was applied by using an online questionnaire. Participants were recruited from the Flemish Patients’ Platform (Vlaams Patiëntenplatform). The Flemish Patients’ Platform is an independent organization founded in 1999, which unites more than 100 patient associations, representing numerous types of chronic conditions. Sampling was opportunistic, based on opting-in and within the constraints of the following inclusion criteria: all participants were older than 18 years, have one or multiple chronic conditions, were able and willing to provide informed consent to participate and could fully understand and express themselves in Dutch. The questionnaire was distributed using Qualtrics (website link) between April and September 2016 as an advertisement on the website and in the online newsletter of each patient association. A general reminder was sent 4 weeks after initial announcement. Several steps were taken to mitigate the risk of common method bias, both ex-ante remedies as well as statistical controls after the questionnaires were returned (e.g. during the design and administration stage of the survey, respondents were assured of confidentiality of the study and that there were no right or wrong answers) (12). Questionnaire development The final questionnaire consisted of the following four parts: (i) socio-demographic characteristics; (ii) medical consumption; (iii) the EuroQol 5D-5L survey and (iv) the PACIC survey. First, selected patient characteristics included gender, age, educational level and type plus number of chronic conditions. The second part contained questions about the patients’ medical consumption. Participants were asked to indicate the number of contact moments (including visits and consultations) with specialists, general practitioners, allied healthcare professionals (e.g. physiotherapist), family caregivers and/or informal caregivers during the last 6 months. To assess respondents’ quality of life perception, the EuroQol Group’s EQ-5D 5L dimensions and Visual Analogue Scale (EQ VAS) were used in part three (13). The EQ-5D 5L has five dimensions (‘mobility’, ‘self-care’, ‘usual activities’, ‘pain/discomfort’ and ‘anxiety/depression’), each of which was reported in one of the five levels: (i) no problems; (ii) slight problems; (iii) moderate problems; (iv) severe problems and (v) extreme problems. The EQ VAS allowed respondents to mark their perceived health status on a scale, ranging from 100 (best imaginable health state) to 0 (worst imaginable health state). Finally, the PACIC instrument was used to assess the quality of chronic care from the patients’ perspective (8). The PACIC survey is a 20-item validated questionnaire, assessing the following scale constructs: ‘patient activation’ (3 items), ‘delivery system design/decision support’ (3 items), ‘goal setting/tailoring’ (5 items), ‘problem-solving/contextual counselling’ (4 items) and ‘follow-up/coordination’ (5 items). Respondents were asked to evaluate chronic care (received during the last 6 months) on a five-point Likert scale, ranging from 1 (none of the time) to 5 (always) with higher scores indicating better patient-assessed quality of care and greater alignment with the CCM. Statistical analysis Analyses were performed using SPSS software version 23. The significance level α was set at 0.05 and all P values were two-sided. The analyses and descriptions followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies (14). First, descriptive statistics were used to determine the sample’s characteristics, medical consumption and quality of life. The EQ-5D-5L levels were dichotomized into ‘no problems’ (level 1) and ‘problems’ (levels 2 to 5) as suggested by the EuroQol User Guide EQ-5D (15). The PACIC survey has been translated and validated in several studies. However, validation studies showed mixed evidence regarding data quality and properties of the PACIC scales (16). Therefore, the present study sought to conduct a confirmatory factor analysis to test the hypothesized factor structure of the PACIC survey before interpreting the 20-item scale in a Belgian population. The five-domain structure of the PACIC survey was explored by conducting a confirmatory factor analysis using R: A Language and Environment for Statistical Computing version 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria) (17). To test the measurement models, the indices for model fit were used with cut-off criteria that were proposed by Hu and Bentler (18). Afterwards, each PACIC subscale was scored by averaging items completed within the scale and the overall PACIC score as an average across all 20 items (8). Analyses of differences in mean PACIC score were performed with the independent sample T test and the one-way ANOVA test. The Spearman correlation test was conducted to calculate bivariate correlations. Since all questionnaires were completely filled out, imputation of missing data was not necessary. Ethical consideration Participants were informed that the collected information would be kept confidential and that the questionnaire was anonymous. There were no incentives provided for completing the questionnaire. The institutional ethics committees of Hasselt University and Ghent University reviewed and approved the study (ref. CME2016/0122). Results Respondents’ characteristics A total of 339 questionnaires were returned. Table 1 presents sample characteristics. The mean age for the entire sample was 55.80 years (SD ± 11.76) and the majority of respondents were female (65.2%, n = 221). More than half of the respondents hold a college or university degree (53.7%, n = 182). The median number of chronic conditions was 2, ranging from 1 to 9 chronic conditions. The top five most prevalent chronic conditions were chronic back pain (31.3%, n = 106), multiple sclerosis (26.8%, n = 91), chronic neck pain (23.3%, n = 79), osteoarthritis (22.7%, n = 77) and hypertension (15.6%, n = 53). Table 1. Sample demographics for chronic patients included in the analysis (n = 339, year, 2016) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) View Large Table 1. Sample demographics for chronic patients included in the analysis (n = 339, year, 2016) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) View Large Medical consumption Table 2 displays respondents’ medical consumption. Respondents had five monthly contacts with their healthcare team (range, 0–95). Monthly visits to a general practitioner (median, 1; range, 0–12) and a medical specialist (median, 1; range, 0–12) were most prevalent. Related to the most frequent reported chronic conditions, the following medical specialists were visited most frequently: neurologist (36.2%, n = 149), rheumatologist (13.3%, n = 55) and pulmonologist (11.9%, n = 49). The majority of patients having home care received their care delivery for more than 1 year (28%, n = 95) and mainly hygienic care (37.2%, n = 48), followed by injections (20.9%, n = 27), wound care (14.7%, n = 19), managing and administering medication (12.4%, n = 16), help with transfers (10.9%, n = 14) and catheter care (3.9%, n = 5). Finally, 42.2% (n = 143) received monthly family/informal care, with a median of 40 h per month. Table 2. Sample’s medical consumption and quality of life (n = 339, year, 2016) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) View Large Table 2. Sample’s medical consumption and quality of life (n = 339, year, 2016) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) View Large Quality of life Regarding quality of life, the median EQ 5D-5L VAS score was 60 (range, 0–95). Chronically ill patients experienced the most problems with pain/discomfort (86.7%, n = 294), followed by usual activities (80.5%, n = 273), mobility (65.5%, n = 222), self-care (48.1%, n = 163) and anxiety/depression (46.0%, n = 156). Details are provided in Table 2. Confirmatory factor analysis of the PACIC survey For the confirmatory factor analysis, 339 respondents with no missing data were included. The indices for model fit (see Supplementary Table S1) showed that the data fit well (18): the comparative fit index was 0.902, the tucker lewis index was 0.887, the root mean square error of approximation was 0.085 and the standardized root mean square residual was 0.060. However, the chi-square statistic test was significant (x2=558.746, df = 165, P < 0.001). Nevertheless, it tends to result in a rejection of the model in large samples (over 200 cases) and is therefore sensitive to sample size (19). The confirmatory factor analysis (see Supplementary Table S2) showed high factor loadings for items in the scales ‘delivery system design/decision support’ (range, 0.62–0.80), ‘patient activation’ (range, 0.66–0.84) and ‘problem solving/contextual counselling’ (range, 0.75–0.89). The remainders of the PACIC scales included items with both moderate and high loadings. In conclusion, all factor loadings were above the 0.50 cut-off value (18). PACIC overall and subscales scores The mean overall PACIC score was 2.87 (SD ± 0.93) on a maximum score of 5. The highest mean score for the PACIC subscales was found for ‘patient activation’ (M = 3.26, SD ± 1.12), followed by ‘delivery system design/decision support’ (M = 3.23, SD ± 0.99), ‘problem solving/contextual counselling’ (M = 2.86, SD ± 1.17), ‘goal setting/tailoring’ (M = 2.70, SD ± 1.00) and ‘follow-up/coordination’ (M = 2.59, SD ± 1.03). Association between PACIC scores and respondents’ characteristics, medical consumption and quality of life Tables 3 and 4 show the association between the mean PACIC score and respondents’ socio-demographic characteristics, medical consumption and quality of life. Quality of life, as measured by the EQ-5D VAS, was found to have a significantly positive correlation with the mean PACIC score (Spearman correlation = 0.153, P = 0.005). The following characteristics were not associated with a significant difference in mean PACIC score: respondents’ characteristics (including gender, age, educational level and number of chronic conditions), medical consumption (including number of contact moments with the healthcare team, number of professionals in the healthcare team, duration of home care and number of hours family and/or informal care) and quality of life (EQ-5D) dimensions (including mobility, self-care, usual activities, pain/discomfort and anxiety/depression). Table 3. Univariate analyses of the variables regarding sample’s demographics, medical consumption and quality of life in relation to the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 PACIC, Patient Assessment of Chronic Illness Care. View Large Table 3. Univariate analyses of the variables regarding sample’s demographics, medical consumption and quality of life in relation to the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 PACIC, Patient Assessment of Chronic Illness Care. View Large Table 4. Correlations between the variables regarding sample’s demographics, medical consumption, and quality of life and the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 *Pearson correlation. PACIC, Patient Assessment of Chronic Illness Care. View Large Table 4. Correlations between the variables regarding sample’s demographics, medical consumption, and quality of life and the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 *Pearson correlation. PACIC, Patient Assessment of Chronic Illness Care. View Large Discussion The rising prevalence of patients with chronic diseases represents substantial challenges in delivering high-quality care (1). Comprehensive, integrated and patient-centred strategies are crucial to improve chronic care (20). A critical step in developing these new and innovative strategies is assessing the needs and preferences of chronic patients. Therefore, the current study used the PACIC survey to explore patients’ perspective of today’s chronic illness care and the alignment with the CCM in Belgium (Flanders). A total of 339 chronic patients completed the survey. The top five most prevalent chronic conditions were chronic back pain, multiple sclerosis, chronic neck pain, osteoarthritis and hypertension. Consequently, the study population reflected the top five leading causes of years lived with disability in Belgium: low back pain, cerebrovascular diseases, falls, neck pain and other musculoskeletal diseases (21). Additionally, the current study found a mean PACIC score of 2.87 on a maximum score of 5. The highest PACIC subscale scores were found for ‘patient activation’ and ‘delivery system design/decision support’, suggesting that chronic patients are generally active patients who are well supported and motivated by their healthcare professionals. Lowest PACIC subscale scores were found for ‘goal setting/tailoring’ and ‘follow-up/coordination’, indicating that chronic patients experience a lack of setting specific goals for their care delivery and in arranging follow-ups. Furthermore, no relationship was observed between PACIC scores and patients’ socio-demographic characteristics, medical consumption and quality of life (EQ-5D) dimensions. However, quality of life, as measured by the EQ-5D VAS, had a significantly positive correlation with the mean PACIC score. This finding, also found by Randell et al. and Schmittdiel et al., suggests that implementing quality improvements in chronic care may benefit the perceived health state of patients with chronic diseases (22,23). Given that quality adjusted life years are the main measure of benefit in cost-effectiveness models, the authors have chosen the VAS scale as it is the most regularly and user-friendly tool used for eliciting preferences (15). The results of the present study are in line with previous research (23–26), however inferior to the mean score of Glasgow et al. and Balbale et al. (27,28). Differences in mean PACIC scores may be attributable to the fact that some studies focused on specific chronic conditions (10,22,25,27,28) or healthcare settings (23,24). Houle et al. evaluated chronic illness care among Canadian patients and obtained a mean PACIC score of 2.80, indicating that CCM-concordant care occurred ‘a little or some of the time’ (24). Petersen et al. described how older patients with multimorbidity assessed routine chronic care in Germany and found an overall mean PACIC score of 2.40 (10). Furthermore, the mean PACIC score for seven Kaiser Permanente regions in the USA was 2.70 (23) and 3.05 among American veterans with multiple chronic conditions (28). Finally, the mean PACIC score in a large inflammatory bowel disease cohort (22), for diabetic patients (27) and for patients with osteoarthritis (25) was 2.40, 3.20 and 2.79, respectively. Chronic patients are in high need of long-term care which brings together a broad range of healthcare professionals that moreover integrates and coordinates services along the continuum of care (29). As a result, integrated delivery systems have gathered momentum to correct deficiencies in current chronic care such as lack of care coordination (30). Integrated care is considered an appropriate answer in potentially reducing the fragmentation of care, improving the quality of care and controlling healthcare-related costs (5,6). Consequently, there has been an increase in the initiatives to encourage professional partnerships such as the Integrated Care Strategies in Australia, the New Care Models and Integrated Care Pioneers in England and the Population Health Management Pilots in the Netherlands (31–33). In Belgium, a large national programme on integrated care is also launched, called Integrated Care for a Better Health. Within this programme, 20 pilot projects were selected for further conceptualization (34). These projects may also use the PACIC survey or the results of the present study to formalize their innovative and integrated care models with special attention to the patients’ needs and expectations. Limitations The results of the present study have to be interpreted carefully. First, the current study was limited by a cross-sectional study design that prevents examining causality and therefore determining the direction of the observed association between the EuroQol VAS result and the mean PACIC score. In addition, the sample consisted largely of members of patient organizations who are dedicated and committed within a strong involvement in their care. This could explain the high score on the subscale ‘patient activation’. Third, the five PACIC subscales do not perfectly map onto the six CCM components. According to the developers of the PACIC instrument, most chronic patients may not be aware of some aspects of their care such as clinical information systems (8). Moreover, the present study should also have assessed depressive symptoms and the severity of the chronic disease since previous research indicated that PACIC scores may be associated with depression and the chronic disease burden (26). Finally, it is of great importance to assess both patients’ and healthcare professionals’ perspectives when evaluating quality of chronic illness care as the ACIC (Assessment of Chronic Illness Care) and PACIC scales appear to provide complementary information (35). This study also has some important strengths. First, a mixed sample of chronic conditions was included. Furthermore, patients’ perspective of chronic illness care in Belgium has, to the best of the authors’ knowledge, not yet been published in scientific literature. Finally, the present study conducted a confirmatory factor analysis to test the hypothesized factor structure of the PACIC instrument in a Belgian population. Conclusion Long-term, structured and proactive approaches of care may help to reduce the burden of chronic diseases. The CCM is considered an important step towards improved care for patients with chronic diseases. Findings of this study showed that CCM elements have not yet been fully implemented in today’s chronic illness care in Belgium (Flanders). Elements such as dealing with problems which interfered with achieving predefined goals (‘problem solving/contextual counselling’), helping patients to set specific goals (‘goal setting/tailoring’) and arranging follow-ups (‘follow-up/coordination’) are less common in today’s care for chronic patients. Supplementary material Supplementary material is available at Family Practice online. Funding: This work was supported by ‘Agentschap Innoveren & Ondernemen’ (Belgium). Ethics approval: Institutional ethics committees Hasselt University and Ghent University. Conflict of interest: None. Acknowledgements The authors would like to thank all participants in the study who gave their time and shared their valuable views. They would also like to thank the following individuals for their contribution towards data collection: Ilse Weeghmans (MSc) and Roel Heijlen (MSc) from The Flemish Patients’ Platform vzw. Written permission from all persons named in the Acknowledgement was obtained. References 1. Wang H , Naghavi M , Allen C et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015 . Lancet 2016 ; 388 : 1459 – 544 . Google Scholar CrossRef Search ADS PubMed 2. Epping-Jordan JE , Pruitt SD , Bengoa R , Wagner EH . Improving the quality of health care for chronic conditions . Qual Saf Health Care 2004 ; 13 : 299 – 305 . Google Scholar CrossRef Search ADS PubMed 3. Davy C , Bleasel J , Liu H , Tchan M , Ponniah S , Brown A . Effectiveness of chronic care models: opportunities for improving healthcare practice and health outcomes: a systematic review . 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Randell RL , Long MD , Martin CF et al. Patient perception of chronic illness care in a large inflammatory bowel disease cohort . Inflamm Bowel Dis 2013 ; 19 : 1428 – 33 . Google Scholar CrossRef Search ADS PubMed 23. Schmittdiel J , Mosen DM , Glasgow R , Hibbard J , Remmers C , Bellows J . Patient Assessment of Chronic Illness Care (PACIC) and improved patient-centered outcomes for chronic conditions . J Gen Intern Med 2008 ; 23 : 77 – 80 . Google Scholar CrossRef Search ADS PubMed 24. Houle J , Beaulieu MD , Lussier MT et al. Patients’ experience of chronic illness care in a network of teaching settings . Can Fam Phys 2012 ; 58 : 1366 – 73 . 25. Rosemann T , Laux G , Szecsenyi J , Grol R . The Chronic Care Model: congruency and predictors among primary care patients with osteoarthritis . Qual Saf Health Care 2008 ; 17 : 442 – 6 . Google Scholar CrossRef Search ADS PubMed 26. Cramm JM , Nieboer AP . The chronic care model: congruency and predictors among patients with cardiovascular diseases and chronic obstructive pulmonary disease in the Netherlands . BMC Health Serv Res 2012 ; 12 : 242 . Google Scholar CrossRef Search ADS PubMed 27. Glasgow RE , Whitesides H , Nelson CC , King DK . Use of the Patient Assessment of Chronic Illness Care (PACIC) with diabetic patients: relationship to patient characteristics, receipt of care, and self-management . Diabetes Care 2005 ; 28 : 2655 – 61 . Google Scholar CrossRef Search ADS PubMed 28. Balbale SN , Etingen B , Malhiot A , Miskevics S , LaVela SL . Perceptions of chronic illness care among veterans with multiple chronic conditions . Mil Med 2016 ; 181 : 439 – 44 . Google Scholar CrossRef Search ADS PubMed 29. Nolte E , McKee M. Caring for People with Chronic Conditions – a Health Systems Perspective . Berkshire, UK : The European Observatory on Health Systems and Policies , 2009 ; 71 : A127 . 30. Goodwin N , Smith J , Davies A et al. Report to the Department of Health and NHS Future Forum: Integrated Care for Patients and Populations: Improving Outcomes by Working Together . London : The King’s Fund , 2012 . 31. NSW Government Health . Integrated Care Strategy . Sydney: NSW Government Health , 2015 : 1 – 21 . PubMed PubMed 32. NHS England . Evaluation Strategy for New Model Vanguards . London: NHS England , 2016 : 1 – 16 . 33. Hejink R , Drewes H , Struijs J , Baan C. Landelijke Monitor Populatiemanagement: Deel 2: ontwerprapport . Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu , 2014 , pp. 1 – 56 . 34. FOD Volksgezondheid . Geïntegreerde Zorg Voor Een Betere Gezondheid . Brussels , 2016 . http://www.integreo.be/nl. 35. Noël PH , Parchman ML , Palmer RF et al. Alignment of patient and primary care practice member perspectives of chronic illness care: a cross-sectional analysis . BMC Fam Pract 2014 ; 15 : 57 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2017. 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Seen through the patients’ eyes: quality of chronic illness care

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Abstract

Abstract Background Most well-developed healthcare systems are facing the challenge of managing the increasing prevalence of patients with chronic diseases. Comprehensive frameworks, such as the chronic care model (CCM), receive widespread acceptance for improving care processes, clinical outcomes and costs. Objective The purpose of this study was to explore chronic patients’ perceptions of the quality of chronic care and the alignment with the CCM. Since previous research indicated that a patient’s assessment may depend on socio-demographic or disease-related characteristics, the relationship between the mean Patient Assessment of Chronic Illness Care (PACIC) score and possible aforementioned predictors was also explored. Methods An observational, cross-sectional study design was applied, and participants were recruited from the Flemish Patients’ Platform (Belgium). An online questionnaire was designed to assess chronic patients’ socio-demographic characteristics, medical consumption, quality of life (EuroQol-5D survey) and the perspective of chronic illness care PACIC survey. Results The mean overall PACIC score was 2.87 on a maximum score of 5. The highest mean score for the PACIC subscales was found for ‘patient activation’ (3.26), followed by ‘delivery system design/decision support’ (3.23), ‘problem solving/contextual counselling’ (2.86), ‘goal setting/tailoring’ (2.70) and ‘follow-up/coordination’ (2.59). Quality of life, as measured by the EuroQol Visual Analogue Scale, had a significantly positive correlation with the mean PACIC score (P = 0.005). Conclusion The CCM is considered an important step towards improved care for patients with chronic diseases. However, the findings of this study showed that elements from the CCM have not yet been fully implemented. Aspects such as dealing with problems which interfered with achieving predefined goals, helping patients to set specific goals for their care delivery and arranging follow-ups are less common in today’s care of chronic diseases. Chronic care, patient preference, quality improvement, quality of care, quality of life Introduction Tremendous progress has been made in healthcare which resulted in large falls in death rates for many life-threatening conditions such as HIV/AIDS, heart attacks and strokes (1). As a result, life expectancy at birth around the globe increased (1). According to the latest study in The Lancet, the Global Burden of Disease Study published in 2015, the world population has gained more than 10 years of life expectancy since 1980 (rising to 69.0 years for men and 74.8 years for women in 2015) (1). The progress in healthcare is worthy of praise, but the future sustainability of healthcare systems is nevertheless jeopardized. Although overall life expectancy has increased by 10.1 years between 1980 and 2015, healthy life expectancy has increased steadily by 6.1 years, which results in more years of life with illness and disability and a consequential high burden on individuals’ quality of life (1). Current delivery of care is, however, often fragmented and largely built around the long-standing acute and episodic model of care, although solid evidence shows that a more integrated and proactive approach helps to reduce the burden of many chronic diseases (2). Comprehensive frameworks, such as the chronic care model (CCM), increasingly receive widespread acceptance for improving care processes, clinical outcomes and healthcare-associated costs (3). The CCM is an evidence-based framework to guide chronic care delivery that supports patient self-management. The framework is structured around integrated healthcare teams and incorporates clinical information systems to facilitate productive patient–professional relationships and to enhance chronic care (4). The CCM describes six elements of a healthcare system that collaboratively encourage high-quality chronic care delivery: (i) organization of healthcare, (ii) clinical information systems, (iii) delivery system design, (iv) decision support, (v) self-management support and (vi) community linkages (4). Implementation of the CCM has been found to improve patient outcomes and reduce healthcare costs (5,6). There is growing consensus that patients can play a more active role in improving healthcare as they increasingly recognize the defects in their own care (7). The Patient Assessment of Chronic Illness Care (PACIC) survey is designed to assess patients’ experiences regarding continuity of chronic care delivery (8). The survey is proven to be an effective instrument to measure the alignment of chronic care with the CCM, that is measuring care that is patient-centred, proactive, planned and includes collaborative goal setting, problem-solving and follow-up support (9). Objectives The aim of the present study was to assess the quality of current chronic care delivery among patients living in Belgium (Flanders). Since previous research indicated that a patient’s assessment may depend on socio-demographic or disease-related characteristics, the relationship between the mean PACIC score and possible aforementioned predictors was also explored (10). Methods The current study is part of CORTEXS (Care Organisation: a Re-Thinking EXpedition in search for Sustainability), an extensive multidisciplinary research project in Flanders (Belgium), that studies integrated care from the micro-level of care recipients and their caregivers, over the meso-level of intra- and inter-organizational processes, to the macro-level of legal and financial frameworks (11). Design and recruitment An observational, cross-sectional study design was applied by using an online questionnaire. Participants were recruited from the Flemish Patients’ Platform (Vlaams Patiëntenplatform). The Flemish Patients’ Platform is an independent organization founded in 1999, which unites more than 100 patient associations, representing numerous types of chronic conditions. Sampling was opportunistic, based on opting-in and within the constraints of the following inclusion criteria: all participants were older than 18 years, have one or multiple chronic conditions, were able and willing to provide informed consent to participate and could fully understand and express themselves in Dutch. The questionnaire was distributed using Qualtrics (website link) between April and September 2016 as an advertisement on the website and in the online newsletter of each patient association. A general reminder was sent 4 weeks after initial announcement. Several steps were taken to mitigate the risk of common method bias, both ex-ante remedies as well as statistical controls after the questionnaires were returned (e.g. during the design and administration stage of the survey, respondents were assured of confidentiality of the study and that there were no right or wrong answers) (12). Questionnaire development The final questionnaire consisted of the following four parts: (i) socio-demographic characteristics; (ii) medical consumption; (iii) the EuroQol 5D-5L survey and (iv) the PACIC survey. First, selected patient characteristics included gender, age, educational level and type plus number of chronic conditions. The second part contained questions about the patients’ medical consumption. Participants were asked to indicate the number of contact moments (including visits and consultations) with specialists, general practitioners, allied healthcare professionals (e.g. physiotherapist), family caregivers and/or informal caregivers during the last 6 months. To assess respondents’ quality of life perception, the EuroQol Group’s EQ-5D 5L dimensions and Visual Analogue Scale (EQ VAS) were used in part three (13). The EQ-5D 5L has five dimensions (‘mobility’, ‘self-care’, ‘usual activities’, ‘pain/discomfort’ and ‘anxiety/depression’), each of which was reported in one of the five levels: (i) no problems; (ii) slight problems; (iii) moderate problems; (iv) severe problems and (v) extreme problems. The EQ VAS allowed respondents to mark their perceived health status on a scale, ranging from 100 (best imaginable health state) to 0 (worst imaginable health state). Finally, the PACIC instrument was used to assess the quality of chronic care from the patients’ perspective (8). The PACIC survey is a 20-item validated questionnaire, assessing the following scale constructs: ‘patient activation’ (3 items), ‘delivery system design/decision support’ (3 items), ‘goal setting/tailoring’ (5 items), ‘problem-solving/contextual counselling’ (4 items) and ‘follow-up/coordination’ (5 items). Respondents were asked to evaluate chronic care (received during the last 6 months) on a five-point Likert scale, ranging from 1 (none of the time) to 5 (always) with higher scores indicating better patient-assessed quality of care and greater alignment with the CCM. Statistical analysis Analyses were performed using SPSS software version 23. The significance level α was set at 0.05 and all P values were two-sided. The analyses and descriptions followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies (14). First, descriptive statistics were used to determine the sample’s characteristics, medical consumption and quality of life. The EQ-5D-5L levels were dichotomized into ‘no problems’ (level 1) and ‘problems’ (levels 2 to 5) as suggested by the EuroQol User Guide EQ-5D (15). The PACIC survey has been translated and validated in several studies. However, validation studies showed mixed evidence regarding data quality and properties of the PACIC scales (16). Therefore, the present study sought to conduct a confirmatory factor analysis to test the hypothesized factor structure of the PACIC survey before interpreting the 20-item scale in a Belgian population. The five-domain structure of the PACIC survey was explored by conducting a confirmatory factor analysis using R: A Language and Environment for Statistical Computing version 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria) (17). To test the measurement models, the indices for model fit were used with cut-off criteria that were proposed by Hu and Bentler (18). Afterwards, each PACIC subscale was scored by averaging items completed within the scale and the overall PACIC score as an average across all 20 items (8). Analyses of differences in mean PACIC score were performed with the independent sample T test and the one-way ANOVA test. The Spearman correlation test was conducted to calculate bivariate correlations. Since all questionnaires were completely filled out, imputation of missing data was not necessary. Ethical consideration Participants were informed that the collected information would be kept confidential and that the questionnaire was anonymous. There were no incentives provided for completing the questionnaire. The institutional ethics committees of Hasselt University and Ghent University reviewed and approved the study (ref. CME2016/0122). Results Respondents’ characteristics A total of 339 questionnaires were returned. Table 1 presents sample characteristics. The mean age for the entire sample was 55.80 years (SD ± 11.76) and the majority of respondents were female (65.2%, n = 221). More than half of the respondents hold a college or university degree (53.7%, n = 182). The median number of chronic conditions was 2, ranging from 1 to 9 chronic conditions. The top five most prevalent chronic conditions were chronic back pain (31.3%, n = 106), multiple sclerosis (26.8%, n = 91), chronic neck pain (23.3%, n = 79), osteoarthritis (22.7%, n = 77) and hypertension (15.6%, n = 53). Table 1. Sample demographics for chronic patients included in the analysis (n = 339, year, 2016) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) View Large Table 1. Sample demographics for chronic patients included in the analysis (n = 339, year, 2016) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) Characteristic Mean (SD), n (%) or median (range) Age (years), mean (SD) 55.80 (11.76) Gender, n (%)  Female 221 (65.2)  Male 118 (34.8) Educational level, n (%)  Less than high school 22 (6.5)  High school 134 (39.5)  College 134 (39.5)  University 49 (14.5) Number of chronic conditions, median (range) 2.00 (1–9) Five most prevalent chronic conditions, n (%)  Chronic back pain 106 (31.3)  Multiple sclerosis 91 (26.8)  Chronic neck pain 79 (23.3)  Osteoarthritis 77 (22.7)  Hypertension 53 (15.6) View Large Medical consumption Table 2 displays respondents’ medical consumption. Respondents had five monthly contacts with their healthcare team (range, 0–95). Monthly visits to a general practitioner (median, 1; range, 0–12) and a medical specialist (median, 1; range, 0–12) were most prevalent. Related to the most frequent reported chronic conditions, the following medical specialists were visited most frequently: neurologist (36.2%, n = 149), rheumatologist (13.3%, n = 55) and pulmonologist (11.9%, n = 49). The majority of patients having home care received their care delivery for more than 1 year (28%, n = 95) and mainly hygienic care (37.2%, n = 48), followed by injections (20.9%, n = 27), wound care (14.7%, n = 19), managing and administering medication (12.4%, n = 16), help with transfers (10.9%, n = 14) and catheter care (3.9%, n = 5). Finally, 42.2% (n = 143) received monthly family/informal care, with a median of 40 h per month. Table 2. Sample’s medical consumption and quality of life (n = 339, year, 2016) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) View Large Table 2. Sample’s medical consumption and quality of life (n = 339, year, 2016) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) Healthcare Median (range) or n (%) Visits healthcare team aggregated (monthly), median (range) 5.00 (0–95) Most prevalent visits to or contacts with, median (range)  General practitioner (monthly) 1.00 (0–12)  Specialist (monthly) 1.00 (0–12)   Neurologist, n (%) 149 (36.2)   Rheumatologist, n (%) 55 (13.3)   Pulmonologist, n (%) 49 (11.9) Number of professionals in healthcare team, median (range) 2.00 (0–8) Duration of home care, n (%)  No home care 201 (59.4)  Less than 6 months 31 (9.1)  Between 6 months and 1 year 12 (3.5)  More than 1 year 95 (28.0) Most prevalent types of care received at home, n (%)  Toilet and hygienic care/washing and dressing 48 (37.2)  Injections 27 (20.9)  Wound care 19 (14.7)  Managing and administering medication 16 (12.4)  Help in and out of bed/help with transfers 14 (10.9)  Catheter care 5 (3.9) Receiving family/informal care  Yes 143 (42.2)  No 196 (57.8) Problems quality of life (EQ-5D) dimensions, n (%)  Mobility 222 (65.5)  Self-care 163 (48.1)  Usual activities 273 (80.5)  Pain/discomfort 294 (86.7)  Anxiety/depression 156 (46.0) Quality of life (EQ-5D) VAS, median (range) 60.00 (0–95) View Large Quality of life Regarding quality of life, the median EQ 5D-5L VAS score was 60 (range, 0–95). Chronically ill patients experienced the most problems with pain/discomfort (86.7%, n = 294), followed by usual activities (80.5%, n = 273), mobility (65.5%, n = 222), self-care (48.1%, n = 163) and anxiety/depression (46.0%, n = 156). Details are provided in Table 2. Confirmatory factor analysis of the PACIC survey For the confirmatory factor analysis, 339 respondents with no missing data were included. The indices for model fit (see Supplementary Table S1) showed that the data fit well (18): the comparative fit index was 0.902, the tucker lewis index was 0.887, the root mean square error of approximation was 0.085 and the standardized root mean square residual was 0.060. However, the chi-square statistic test was significant (x2=558.746, df = 165, P < 0.001). Nevertheless, it tends to result in a rejection of the model in large samples (over 200 cases) and is therefore sensitive to sample size (19). The confirmatory factor analysis (see Supplementary Table S2) showed high factor loadings for items in the scales ‘delivery system design/decision support’ (range, 0.62–0.80), ‘patient activation’ (range, 0.66–0.84) and ‘problem solving/contextual counselling’ (range, 0.75–0.89). The remainders of the PACIC scales included items with both moderate and high loadings. In conclusion, all factor loadings were above the 0.50 cut-off value (18). PACIC overall and subscales scores The mean overall PACIC score was 2.87 (SD ± 0.93) on a maximum score of 5. The highest mean score for the PACIC subscales was found for ‘patient activation’ (M = 3.26, SD ± 1.12), followed by ‘delivery system design/decision support’ (M = 3.23, SD ± 0.99), ‘problem solving/contextual counselling’ (M = 2.86, SD ± 1.17), ‘goal setting/tailoring’ (M = 2.70, SD ± 1.00) and ‘follow-up/coordination’ (M = 2.59, SD ± 1.03). Association between PACIC scores and respondents’ characteristics, medical consumption and quality of life Tables 3 and 4 show the association between the mean PACIC score and respondents’ socio-demographic characteristics, medical consumption and quality of life. Quality of life, as measured by the EQ-5D VAS, was found to have a significantly positive correlation with the mean PACIC score (Spearman correlation = 0.153, P = 0.005). The following characteristics were not associated with a significant difference in mean PACIC score: respondents’ characteristics (including gender, age, educational level and number of chronic conditions), medical consumption (including number of contact moments with the healthcare team, number of professionals in the healthcare team, duration of home care and number of hours family and/or informal care) and quality of life (EQ-5D) dimensions (including mobility, self-care, usual activities, pain/discomfort and anxiety/depression). Table 3. Univariate analyses of the variables regarding sample’s demographics, medical consumption and quality of life in relation to the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 PACIC, Patient Assessment of Chronic Illness Care. View Large Table 3. Univariate analyses of the variables regarding sample’s demographics, medical consumption and quality of life in relation to the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 Characteristics Mean PACIC score (SD) 95% CI for mean P value Mean difference 95% CI of difference Gender Female 2.80 (0.90) 2.683 2.923 0.062 0.199 −0.015 0.415 Male 3.00 (0.96) 2.824 3.182 Age 18-57 2.88 (0.92) 2.747 3.021 0.74 0.034 −0.168 0.236 57-85 2.85 (0.94) 2.701 3 Educational level High school or less 2.94 (0.94) 2.788 3.086 0.186 0.134 −0.065 0.333 College or higher 2.80 (0.91) 2.67 2.937 Number chronic conditions 1 2.95 (0.97) 2.782 3.089 0.243 0.119 −0.081 0.319 ≥ 2 2.82 (0.89) 2.685 2.948 Duration of home care Less than 1 year 3.15 (1.01) 2.834 3.459 0.109 0.273 −0.062 0.608 More than 1 year 2.87 (0.88) 2.695 3.052 QoL dimension mobility Problems 2.94 (1.06) 2.748 3.14 0.301 0.111 −0.1 0.322 No problems 2.83 (0.86) 2.72 2.947 QoL dimension self-care Problems 2.92 (0.99) 2.771 3.067 0.328 0.1 −0.1 0.299 No problems 2.82 (0.86) 2.686 2.953 QoL dimension usual activities Problems 2.84 (1.08) 2.573 3.116 0.8 −0.033 −0.289 0.223 No problems 2.88 (0.89) 2.77 2.984 QoL dimension pain discomfort Problems 3.03 (1.13) 2.692 3.377 0.212 0.188 −0.108 0.483 No problems 2.85 (0.89) 2.743 2.95 QoL dimension ‘anxiety depression’ Problems 2.96 (0.98) 2.813 3.103 0.063 0.19 −0.01 0.389 No problems 2.77 (0.86) 2.632 2.905 PACIC, Patient Assessment of Chronic Illness Care. View Large Table 4. Correlations between the variables regarding sample’s demographics, medical consumption, and quality of life and the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 *Pearson correlation. PACIC, Patient Assessment of Chronic Illness Care. View Large Table 4. Correlations between the variables regarding sample’s demographics, medical consumption, and quality of life and the mean PACIC score (n = 339, year, 2016) Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 Characteristics Mean PACIC score Correlation coefficient P value Age* 0.011 0.839 Number of chronic conditions −0.068 0.217 Number of visits by healthcare team −0.057 0.298 Number of hours of family and informal care −0.039 0.496 Number of professionals in healthcare team −0.059 0.283 QoL Visual Analogue Scale 0.153 0.005 *Pearson correlation. PACIC, Patient Assessment of Chronic Illness Care. View Large Discussion The rising prevalence of patients with chronic diseases represents substantial challenges in delivering high-quality care (1). Comprehensive, integrated and patient-centred strategies are crucial to improve chronic care (20). A critical step in developing these new and innovative strategies is assessing the needs and preferences of chronic patients. Therefore, the current study used the PACIC survey to explore patients’ perspective of today’s chronic illness care and the alignment with the CCM in Belgium (Flanders). A total of 339 chronic patients completed the survey. The top five most prevalent chronic conditions were chronic back pain, multiple sclerosis, chronic neck pain, osteoarthritis and hypertension. Consequently, the study population reflected the top five leading causes of years lived with disability in Belgium: low back pain, cerebrovascular diseases, falls, neck pain and other musculoskeletal diseases (21). Additionally, the current study found a mean PACIC score of 2.87 on a maximum score of 5. The highest PACIC subscale scores were found for ‘patient activation’ and ‘delivery system design/decision support’, suggesting that chronic patients are generally active patients who are well supported and motivated by their healthcare professionals. Lowest PACIC subscale scores were found for ‘goal setting/tailoring’ and ‘follow-up/coordination’, indicating that chronic patients experience a lack of setting specific goals for their care delivery and in arranging follow-ups. Furthermore, no relationship was observed between PACIC scores and patients’ socio-demographic characteristics, medical consumption and quality of life (EQ-5D) dimensions. However, quality of life, as measured by the EQ-5D VAS, had a significantly positive correlation with the mean PACIC score. This finding, also found by Randell et al. and Schmittdiel et al., suggests that implementing quality improvements in chronic care may benefit the perceived health state of patients with chronic diseases (22,23). Given that quality adjusted life years are the main measure of benefit in cost-effectiveness models, the authors have chosen the VAS scale as it is the most regularly and user-friendly tool used for eliciting preferences (15). The results of the present study are in line with previous research (23–26), however inferior to the mean score of Glasgow et al. and Balbale et al. (27,28). Differences in mean PACIC scores may be attributable to the fact that some studies focused on specific chronic conditions (10,22,25,27,28) or healthcare settings (23,24). Houle et al. evaluated chronic illness care among Canadian patients and obtained a mean PACIC score of 2.80, indicating that CCM-concordant care occurred ‘a little or some of the time’ (24). Petersen et al. described how older patients with multimorbidity assessed routine chronic care in Germany and found an overall mean PACIC score of 2.40 (10). Furthermore, the mean PACIC score for seven Kaiser Permanente regions in the USA was 2.70 (23) and 3.05 among American veterans with multiple chronic conditions (28). Finally, the mean PACIC score in a large inflammatory bowel disease cohort (22), for diabetic patients (27) and for patients with osteoarthritis (25) was 2.40, 3.20 and 2.79, respectively. Chronic patients are in high need of long-term care which brings together a broad range of healthcare professionals that moreover integrates and coordinates services along the continuum of care (29). As a result, integrated delivery systems have gathered momentum to correct deficiencies in current chronic care such as lack of care coordination (30). Integrated care is considered an appropriate answer in potentially reducing the fragmentation of care, improving the quality of care and controlling healthcare-related costs (5,6). Consequently, there has been an increase in the initiatives to encourage professional partnerships such as the Integrated Care Strategies in Australia, the New Care Models and Integrated Care Pioneers in England and the Population Health Management Pilots in the Netherlands (31–33). In Belgium, a large national programme on integrated care is also launched, called Integrated Care for a Better Health. Within this programme, 20 pilot projects were selected for further conceptualization (34). These projects may also use the PACIC survey or the results of the present study to formalize their innovative and integrated care models with special attention to the patients’ needs and expectations. Limitations The results of the present study have to be interpreted carefully. First, the current study was limited by a cross-sectional study design that prevents examining causality and therefore determining the direction of the observed association between the EuroQol VAS result and the mean PACIC score. In addition, the sample consisted largely of members of patient organizations who are dedicated and committed within a strong involvement in their care. This could explain the high score on the subscale ‘patient activation’. Third, the five PACIC subscales do not perfectly map onto the six CCM components. According to the developers of the PACIC instrument, most chronic patients may not be aware of some aspects of their care such as clinical information systems (8). Moreover, the present study should also have assessed depressive symptoms and the severity of the chronic disease since previous research indicated that PACIC scores may be associated with depression and the chronic disease burden (26). Finally, it is of great importance to assess both patients’ and healthcare professionals’ perspectives when evaluating quality of chronic illness care as the ACIC (Assessment of Chronic Illness Care) and PACIC scales appear to provide complementary information (35). This study also has some important strengths. First, a mixed sample of chronic conditions was included. Furthermore, patients’ perspective of chronic illness care in Belgium has, to the best of the authors’ knowledge, not yet been published in scientific literature. Finally, the present study conducted a confirmatory factor analysis to test the hypothesized factor structure of the PACIC instrument in a Belgian population. Conclusion Long-term, structured and proactive approaches of care may help to reduce the burden of chronic diseases. The CCM is considered an important step towards improved care for patients with chronic diseases. Findings of this study showed that CCM elements have not yet been fully implemented in today’s chronic illness care in Belgium (Flanders). Elements such as dealing with problems which interfered with achieving predefined goals (‘problem solving/contextual counselling’), helping patients to set specific goals (‘goal setting/tailoring’) and arranging follow-ups (‘follow-up/coordination’) are less common in today’s care for chronic patients. Supplementary material Supplementary material is available at Family Practice online. Funding: This work was supported by ‘Agentschap Innoveren & Ondernemen’ (Belgium). Ethics approval: Institutional ethics committees Hasselt University and Ghent University. Conflict of interest: None. Acknowledgements The authors would like to thank all participants in the study who gave their time and shared their valuable views. 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Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Family PracticeOxford University Press

Published: Dec 10, 2017

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