Received: 11 January 2023 Revised: 22 July 2023 Accepted: 21 August 2023 DOI: 10.1002/hpm.3701 RESEARCH ARTICLE How much convergence exists between registered process measures and patient reported experience measures? A study on Catalan primary healthcare Xavier Ballart | Guillem Rico Departament de Ciència Política, Universitat Autonoma de Barcelona, Bellaterra, Spain Abstract The use of quality measures is important for transparency Correspondence Xavier Ballart, Departament de Ciència and the continuous improvement of performance. However, Política, Universitat Autonoma de Barcelona, we do not know enough about the relationship between Campus de la UAB, Bellaterra 08193, Spain. Email: Xavier.Ballart@uab.cat registered process measures and patient reported expe- rience measures (PREMs) in primary healthcare. Recent Funding information studies point to areas of convergence that run contrary Ministerio de Economía y Competitividad, Grant/Award Number: CSO2017-86653-P anticipated trends. This is a relevant question for healthcare management and governments as their position is stronger when system's guidelines and targets also matter to patients or, vice versa, when patient-centered measures are used to develop new process measures. Our aim is to study both type of measures, their logic and their level of convergence. We also assess the relationship between PREMs, patients' demographic characteristics and area socioeconomic level. We estimated pairwise correlations with patient experience aggregated at the health-center level and a series of multi- level regression models to assess the adjusted effect of four registered process measures on ratings of patient experience at the patient level. We use patient experience as meas- ured by survey data, two composite quality indices and two single indicators regularly computed by the Catalan Health Service. Continuity of care with the same doctor and acces- sibility are positively associated with patient experience. No This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. The International Journal of Health Planning and Management published by John Wiley & Sons Ltd. 1772 Int J Health Plann Mgmt. 2023;38:1772–1788. wileyonlinelibrary.com/journal/hpm 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1773 relationship was observed in the index created to measure quality of assistance. The index measuring the quality of prescriptions was positively associated with patient experi- ence but only when analyzed separately. We conclude that registered process measures and PREMs are not entirely independent, hence improvements of the management side have the potential to impact patient experience. KEYWORDS continuity and access, performance, PREMs, primary healthcare, process measures Highlights • PREMs are related to some registered process measures. • Management's position is stronger when the system's targets matter to patients. •  Convergence is observed with continuity of care and timely access. • The process measure of the overall quality of assistance is not significantly related to PREMs, likely because it comprises too many conditions and diagnostic and resolution measures. • The process measure of the quality of prescription is weakly but significantly related to PREMs, which is an interesting line of inquiry for further research. 1 | INTRODUCTION Primary Health Care (PHC) systems in multiple countries have developed a combination of registered process meas- ures and patient-reported experience measures (PREMs) in order to evaluate and improve performance. Behind efforts to develop these dual systems is a need to increase transparency at the government level and to improve the quality of services with a continuous process of learning on the side of both providers—whether they are public, private or nonprofit—and governments paying for PHC services. 1 Registered process measures reflect the guidelines and performance targets outlined by health management authorities and governments. These evidenced-based clinical guidelines limit the autonomy of healthcare profes- sionals and focus on the system's objectives at the population level. 2 On the other hand, PREMs provide information about patient experiences based on parameters that patients can observe in their use of the system. PREMs can reflect management objectives regarding patient experience and enable progress towards a process that is more patient centered 3–7; however, they are influenced by factors not necessarily related to the quality of services as defined by management and government entities. The combination of both types of measures is probably the best option for managing PHC services, 1 but we do not know enough about their convergence. There might be aspects of the convergence between registered measures and PREMs that provide a clear signal of healthcare performance. However, there might be cases where measures diverge. Hence, it is important to know when convergence is less likely, whether there is room for convergence, and what the benefits and limitations of each type of measures are. 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1774 BALLART and RICO Previous studies on this issue have focused more on inpatient services than outpatient services. 8–15 Studies on patient satisfaction found patient experience to be independent from surgical process measures and hospital safety. 16,17 Initially, it was believed that there was little opportunity for both types of measures to converge. This viewpoint is encapsulated in the claim that patients answer survey questions based on “concierge services” as opposed to clinical processes. Along these lines, patients' opinions were considered less than credible due to their lack of formal medical training and supposed tendency to focus on aspects that can be easily influenced by factors unrelated to care. Part of the literature has observed a trend of individual empirical beliefs generating a demand for services as patients tend to think that these will make or maintain them healthy. 13,18,19 Another argument is that PREMs reflect patient characteristics such as their age, sex or health status, which can be confounded with factors not directly associated with the quality of processes. 7,19 However, a number of studies on hospital performance have found patient experience or satisfaction to be positively associated with hospitals' registered process measures under certain conditions. 8,11,15,20 According to the review article by Anhang Price et al., 4 better patient experiences are associated with better clinical outcomes, patient safety, and patients' adherence to physician advice. Positive patient experiences appear to reduce unnecessary healthcare use 21 and inpatient complications. 15 Still, other studies reported a negative relationship between patient experience and mortality on the one hand and higher expenditures on the other. 22 In contrast, PHC has received less scholarly attention. Again, while the evidence first seemed to suggest a lack of convergence between registered process measures and global ratings of care among specific groups of patients, 16 an increasing number of studies report significant associations between the two types of measures. Some studies consider the relationships between clinical quality and patients' experience, 23 productivity and patient satisfaction, 24 organizational characteristics of providers and patient satisfaction, 25 and quality and choice of doctor. 26 Recently, Glenngärd and Anell found that Swedish providers with satisfied patients also tend to successfully adhere to meas- ures taken by the government to increase standardization. 2 In a different investigation, they found evidence of covar- iation between registered process measures and the PREMs of accessibility and continuity in primary care. 1 This study considers the association between registered process measures and PREMs in primary care in the Spanish region of Catalonia. Spain is a decentralized state where regions have the authority to manage both hospitals and PHC. The Catalan Institute of Health (ICS) has developed the composite process measures of quality of assistance and quality of prescription. These two indices were created following the logic of standardization. Primary healthcare providers must adapt their practices to a variety of processes regarding health promotion, disease prevention, and the diagnosis and treatment of certain conditions that are significant to the government. In addition to these measures, health authorities collect two other process indicators that have been found to covariate with PREMs 1,19,27: conti- nuity of care (defined here as the extent to which patients are treated by the same doctor) and timely access to care services (defined as access to non-emergency care within 48 h of requesting an appointment). These registered process measures can be used to assess performance across primary healthcare providers and to monitor management objectives. However, their association with PREMs is less well established. Our aim is to examine the association between these objective registered process measures and PREMs. The latter are collected by the Catalan Health Department every 2 years through a patient survey. Since continuity and timeliness can be observed by patients and are conceptually similar to some of the PREMs, we expect them to be positively associated. This is less likely for the quality-of-assistance and quality-of-prescription indicators. 28 2 | METHODS This study combines the registered process measures of primary care providers and PREMs with data about patients' individual characteristics and the socioeconomic condition of the larger area PHC centers serve. Catalan citizens are assigned to a Basic Area of Health, each with its own Primary Healthcare Center. Because the survey data included 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1775 the area identification of respondents, we were able to link patients' individual and area characteristics with the registered process measures of the primary healthcare centers to which they are assigned. 2.1 | Survey data PREMs are collected through a survey administered by the Catalan health telephone service (061 CatSalut Respon) on behalf of the Catalan Health Service (CatSalut) every two years. The sample framework was drawn from the Cata- lan Central Registry of Insured Persons. All patients aged 15 or higher who had visited their usual primary healthcare center during the preceding twelve months were eligible for selection (N = 117,603). Random samples of approx- imately 350 users were extracted within each of the Basic Areas of Health with a target of at least 80 completed interviews from each area. Interviews were conducted by phone using CATI software. The authors created a patients' experience of care index based on the 19 PREM items developed and regularly revised by the Catalan Health Service. 29,30 The items encompass the main dimensions of patient experience identi- fied by the literature, including service accessibility, professional-patient interaction, communication, treatment, and support. 3,19,31 The wording of each of the items is included in the Appendix (Table A1). All items were measured using a 5-point Likert-type scale ranging from 1 to 5, labelled from “very poor” to “excellent” or from “never” to “always,” depending on the item. The composite index shows a satisfactory degree of internal consistency with an estimated Cronbach's α of 0.90. The scale was recoded to range from 0 to 100, with higher values denoting a more positive experience of care. The complete survey data and documentation are publicly available. 2.2 | Registered process measures We used two composite indicators (the quality of assistance index and the quality of prescription index) and two single indicators (the continuity of care index and the 48-h accessibility index) available to all primary health centers managed by the Catalan Health Service (n = 287). Restrictions apply to the availability of the data on registered process measures, even if these refer to PHC centers rather than individual patients. The data for the current study were provided by the Catalan Health Institute after obtaining the approval of the university ethics committee and the PADRIS program set by the Catalan administration for studies using patient data. The quality-of-assistance index is a composite measure developed by the Catalan Health Institute. It reflects the guidelines and performance targets of the health system and provides a homogeneous standard for comparing PHC centers on their health promotion, health prevention, care of acute problems, and diagnosis and treatment functions. 32 It is based on sixty individual indicators, shown in Table A2 of the Appendix. Some of the indicators are linked to specific programs, such as the promotion of treating older people in their homes. Each measure is calculated from the data included in the patients' clinical histories. For each indicator, there is a measure for detection and a measure for resolution. Detection levels measure the expected prevalence correcting for age distribution, economic deprivation, and rurality. Resolution measures define minimum and maximum levels. The quality of assistance index ranges from 0 to 1000, with higher values indicating better quality of care. The quality-of-prescription index, also developed by the Catalan Health Institute, measures the quality of the drugs prescribed by PHC doctors based on the use pattern considered to be the best therapeutic option for each disease (individual indicators are shown in Table A3 of the Appendix). This index is validated with data from clinical histories and correlates positively with the overall performance of primary health centers and negatively with the expenses generated by the system. Since 2003, it has been used as an incentive for primary health doctors to improve their prescribing by instilling a variable component to doctors' salaries based on improvements to the quality of the drugs they prescribe. 33 This index varies between 0 and 130, with higher values indicating better prescriptions of 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1776 BALLART and RICO drugs in primary care practice. Because it is less complex than the quality-of-assistance index, this process measure may be more easily perceived by patients and is hence more likely to correlate with PREMs. In addition to these composite indices, we also used the common provider-of-care index, an indicator of conti- nuity of care that measures the percentage of a patient's visits with a single doctor, and the 48-hour-accessibility index, which measures the percentage of patients who are visited within 48 h of requesting an appointment in non-emergency cases. 2.3 | Additional explanatory variables In the multivariate models, we adjusted for the effect of additional patient-level and center-level characteristics that can be confounded with the effects of factors associated with the quality of processes. 31,34 As individual predictors, we included a series of demographic variables reported in the survey: sex, age, educational level, and self-perceived level of health (measured on a five-point scale ranging from “poor” to “excellent”). There is extensive literature show- ing that personal characteristics have an influence on satisfaction with public services. In health services research, age and the subjective perception of well-being are important determinants of patient satisfaction 11,34–39, and educa- tion can make a difference in the way patients live and report their care experiences. 38 The socioeconomic status of the area covered by PHC centers may also impact on PREMs. Public primary health services in lower income areas tend to be more congested, and patient's experience of care could be affected by the time dedicated to each person and the level of knowledge that medical personnel have of the patients' histories. 40,41 We thus included a measure of the level of socioeconomic deprivation of the Basic Area of Health as an additional measure at the center level. This measure, according to the Catalan Health Service, is a composite index based on the indicators of age, employment, occupation, education, health, and rurality correlating to the areas' health needs and the centers' level of assistance pressure. The deprivation index ranges from 0 to 100, with higher values denoting more deprivation. 2.4 | Statistical analysis After the exclusion of noneligible cases (those patients residing in areas assigned to centers not directly managed by the Catalan Health Service) and those with missing data on any of the explanatory variables, the final dataset included 22,567 patients and 284 areas/centers. No differences existed between eligible respondents and non-respondents in terms of age and sex. We employed a two-fold analytical approach. First, we examined the association between each of the health centers' reported process measures and the average level of patient care experience in the Basic Health Area where the centers are located. To this end, we use the survey data to calculate the average rating for patient care experience within each area based on samples ranging from 77 to 80 respondents per area/center (mean = 79.5). The unit of analysis is thus the healthcare center and the Basic Area of Health it covers (n = 284). For each objective measure, we constructed a scatterplot of its relationship with the area's average level of care experience and used Pearson's correlation coefficient (r) to assess the strength of the bivariate linear association. Statistical significance was set at p ≤ 0.05. Next, we estimated a series of models to assess the adjusted effect of the four objective registered process measures using patients' ratings of their patient care experience at the patient (i.e., survey respondent) level. Due to the multilevel nature of the data (patients nested within healthcare centers/areas), we fitted hierarchical, random-intercept linear models. In this case, patients constitute the level-1 unit of analysis, whereas health areas/ centers constitute the level-2. This approach allowed us to account for variations in patient care experiences at the individual level and to adjust for the potential effect of individual characteristics (e.g., sex, age, education, perceived 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1777 health) as well as other health area features (e.g., level of deprivation). We first estimated a baseline model including all patient-level variables and the area's socioeconomics. We then added each of the objective measures separately. A final model included all the process measures at the same time. We assessed the strength of associations based on the size and statistical significance of multilevel regression coefficients. This information was complemented with the models' Akaike Information Criterion and R-squared values, particularly the R-squared between areas/centers, which indicates the proportion of variance explained at level 2, at which the objective predictors are measured. 42 All analyses were conducted using STATA version 16.1. 3 | RESULTS Table 1 shows the statistics for the patient sample and the health areas/centers. PREM ratings of PHC centers were moderately positive, with an overall average of 70.9 on the 0–100 scale. Self-perceived health was “good” or better for most of the sample (75%). Primary healthcare centers fare reasonably well in terms of the objective indices of overall quality of care, pharmaceutical prescription, and continuity of care but rather poorly in terms of the percent- age of patients who are visited within 48 h of requesting an appointment (mean = 31.1%), although this variable has a positively skewed distribution with a substantial number of centers achieving much higher percentages. Figure 1 displays the bivariate relationship between the primary health centers' four main registered process measures and the average care experience scores obtained using the PREMs for the corresponding areas. Several find- ings emerge from the analysis. First, while patient experience appears to be unrelated to the general quality-of-care index, it shows a positive and statistically significant correlation with the quality-of-prescription indicator. On the other hand, there is a stronger association of care experience with continuity of care by the same doctor and with the 48-hour-accessibility indicator. As expected, in both cases the correlation is positive and highly statistically signifi- cant, suggesting that continuity of care by the same doctor and timely accessibility substantially enhance patients' experience of care. Table 2 presents the results of the multilevel models of individual perceptions of patients' care experience. Model 1 includes only the control variables, models 2 to 5 separately add each of the four objective registered process measures, and model 6 includes all four (composite indices and indicators) along with covariates. The results of the covariates are consistent across the models. Patient's care experience is positively related to patients' age, although we find that the age group with the most negative ratings is not the youngest but that between 25 and 34 years. Patient experience is significantly more negative among women than among men. Experience scores increase with educational level, respondents with more formal education evaluate their patient care experience more positively than respondents with no completed education. However, patients with a secondary education report a more posi- tive experience than those with a primary education and those with post-secondary education. Care experience also increases with self-reported health status. These estimates indicate that individuals with an “excellent” self-perceived health evaluate their experience nearly 14 points higher than those reporting “poor” health. Finally, living in an area with higher a level of socioeconomic deprivation (and, hence, more assistance pressure) is significantly associated with decreased patient care experience. Models 2 to 5 show that, when included separately, three of the four registered process measures are statistically significant predictors of patient care experience, after adjusting for individual factors and the deprivation level of Basic Health Areas. As suggested by the results of the aggregate analyses, patient experience levels are impervious to differences in the centers' overall quality of assistance as defined by the quality-of-assistance index (Model 2). However, experience scores are significantly associated with the quality of drug prescription (Model 3), continuity of care by the same doctor (Model 4), and patient accessibility to primary health services within 48 h (Model 5). However, only the coefficients for continuity of care and 48-h accessibility remain statistically significant when all predictors are included, as in Model 6. According to this model, a two standard deviation increase 43 in the centers' measures of continuity of care and 48-h accessibility cause an increase of 1.39 and 2.76 points, respectively, in 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1778 BALLART and RICO TA B L E 1 Descriptive statistics for variables in the analysis. Variable Mean SD Min. Q1 Median Q3 Max. n Patient characteristics Experience of care (0–100) 70.9 15.0 0.0 61.8 72.4 81.6 100.0 22,567 Sex 22,567 Male (%) 49.0 22,567 Female (%) 51.0 22,567 Age 22,567 15–24 (%) 8.2 22,567 25–34 (%) 13.5 22,567 35–44 (%) 22.7 22,567 45–54 (%) 17.8 22,567 55–64 (%) 16.8 22,567 65+ (%) 21.0 22,567 Education 22,567 No education (%) 3.1 22,567 Primary (%) 26.8 22,567 Secondary (%) 35.4 22,567 Tertiary (%) 33.4 22,567 Other (%) 1.3 22,567 Perceived health 22,567 Poor (%) 3.6 22,567 Fair (%) 21.4 22,567 Good (%) 44.2 22,567 Very good (%) 21.9 22,567 Excellent (%) 8.9 22,567 Area/center characteristics Experience of care (avg. 0–100) 70,9 3,4 61,7 68,8 71,0 72,9 80,1 284 Area deprivation (0–100) 43.4 14.4 0.9 35.3 43.1 50.9 100.0 284 Quality of care (0–1000) 723.5 77.0 415.4 679.4 728.1 771.5 910.4 284 Quality of prescription (0–130) 81.2 18.1 25.0 71.0 82.0 94.0 130.0 284 Usual provider (0–100) 71.6 7.3 53.9 66.1 72.1 76.2 89.2 284 48 h accessibility (0–100) 31.1 20.3 2.6 18.0 25.3 36.7 98.6 284 patients' experience scores. Model 6 obtains the lowest AIC (indicating a better fit) and the highest level-2 R 2, (0.531). Compared to Model 1, this represents an increase of 37.3% points in the proportion of variance explained at the area/ center level. 4 | DISCUSSION The combined analysis of registered process measures and PREMs provides evidence that system guidelines and performance targets designed with the aim of creating incentives for PHC professionals to adjust their behavior to system goals, limit their autonomy, and focus on the system's objectives at the population level 2 reflect aspects of 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1779 F I G U R E 1 Relationship between centers' objective quality measures and average ratings of patient experience. [Colour figure can be viewed at wileyonlinelibrary.com] healthcare that matter to patients. Additionally, patients form their PREMs in ways that correspond with some, but not all, of the measures that management uses to provide guidelines to professionals and communicate the ways they intend to achieve better quality in PHC. These results provide strong evidence in support of a positive association between continuity of care and timely accessibility with patient experience of care. 1 Although these effects are relatively modest in size, the contribution of these two aspects of performance to patient experience is non-negligible (as compared to other predictors—e.g., the effect of sex falls roughly in between these two). Patients seem to be able to observe and capture the functional and most tangible aspects of the healthcare process at the primary care level. This study did not find a strong relationship between PREMs and the two main composites of registered process measures designed to provide quality of assistance and quality of prescription. The quality-of-prescription index was positively and significantly associated with patient experience but only when included separately from the other regis- tered process measures. The system guidelines are designed to induce primary health professionals to prescribe effi- ciently and well. Most patients have their own empirical beliefs about medicines 18 but have neither clinical knowledge nor training to judge the medicines they are prescribed or the changes to the medicines they are taking. However, these results show that patient experience can improve if patients understand the reasons why they are told to take a specific drug, the basic function of the drug prescribed and the consequences it may have on their health and living conditions. The results ruled out an association between PREMs and the quality-of-assistance index, probably because this index is too broadly defined, includes both detection and resolution levels for too many specific conditions, and is too complex for most patients. At the level of the individual, it is unlikely that patient experience shows a strong association with a complex index system measuring overall quality of assistance. This result is not contradictory with maintaining and developing both types of measures to improve understand- ing of the overall performance of PHC systems. Process measures are necessary for management, can be negotiated, 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1780 BALLART and RICO TA B L E 2 Multilevel regression analysis of individual-level patient care experience. (1) (2) (3) (4) (5) (6) Fixed effects (coefficients) Patient characteristics Sex (female) −2.040*** −2.040*** −2.040*** −2.038*** −2.030*** −2.029*** (0.190) (0.190) (0.190) (0.190) (0.190) (0.190) Age (ref. = 15–24) 25–34 −1.881*** −1.881*** −1.886*** −1.890*** −1.875*** −1.887*** (0.421) (0.421) (0.421) (0.421) (0.420) (0.420) 35–44 −0.582 −0.581 −0.583 −0.582 −0.592 −0.593 (0.389) (0.389) (0.389) (0.389) (0.389) (0.389) 45–54 1.893*** 1.893*** 1.893*** 1.888*** 1.886*** 1.882*** (0.408) (0.408) (0.408) (0.408) (0.408) (0.408) 55–64 3.100*** 3.100*** 3.095*** 3.093*** 3.081*** 3.073*** (0.420) (0.420) (0.420) (0.420) (0.420) (0.420) 65+ 3.843*** 3.845*** 3.841*** 3.828*** 3.801*** 3.789*** (0.422) (0.422) (0.422) (0.422) (0.422) (0.422) Education (ref. = other) No education −0.180 −0.182 −0.184 −0.175 −0.115 −0.118 (0.989) (0.989) (0.989) (0.988) (0.988) (0.988) Primary 2.426** 2.424** 2.426** 2.397** 2.403** 2.387** (0.845) (0.845) (0.845) (0.845) (0.844) (0.844) Secondary 3.379*** 3.378*** 3.379*** 3.351*** 3.372*** 3.354*** (0.840) (0.840) (0.840) (0.840) (0.840) (0.840) Tertiary 2.876*** 2.876*** 2.877*** 2.855*** 2.888*** 2.872*** (0.844) (0.844) (0.844) (0.843) (0.843) (0.843) Health (ref. = poor) Fair 2.378*** 2.379*** 2.386*** 2.354*** 2.362*** 2.351*** (0.540) (0.540) (0.540) (0.540) (0.540) (0.540) Good 4.777*** 4.777*** 4.783*** 4.747*** 4.744*** 4.730*** (0.524) (0.524) (0.524) (0.524) (0.524) (0.524) Very good 10.302*** 10.304*** 10.303*** 10.273*** 10.266*** 10.246*** (0.553) (0.553) (0.553) (0.553) (0.552) (0.552) Excellent 13.784*** 13.786*** 13.787*** 13.756*** 13.762*** 13.742*** (0.602) (0.602) (0.602) (0.602) (0.602) (0.601) Area/center characteristics Deprivation −0.035** −0.036** −0.035** −0.019 −0.039*** −0.028* (0.013) (0.013) (0.013) (0.012) (0.011) (0.011) Quality of care 0.002 −0.002 (0.002) (0.002) 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1781 TA B L E 2 (Continued) (1) (2) (3) (4) (5) (6) Quality of prescription 0.026** 0.017 (0.010) (0.009) Usual provider 0.163*** 0.096*** (0.024) (0.023) 48h accessibility 0.081*** 0.068*** (0.008) (0.008) Constant 63.329*** 61.915*** 61.164*** 50.998*** 61.001*** 54.382*** (1.192) (2.092) (1.450) (2.172) (1.172) (2.433) Random effects (variance components) Health areas/centers 7.167*** 7.144*** 6.941*** 5.829*** 4.454*** 3.990*** (0.814) (0.812) (0.795) (0.701) (0.586) (0.547) Patients 199.998*** 199.998*** 199.998*** 199.998*** 199.997*** 199.998*** (1.895) (1.895) (1.895) (1.895) (1.895) (1.895) Health areas/centers 284 284 284 284 284 284 N 22,567 22,567 22,567 22,567 22,567 22,567 Level-1 R 2 0.072 0.072 0.072 0.072 0.072 0.072 Level-2 R 2 0.158 0.161 0.185 0.316 0.477 0.531 AIC 184,028 184,029 184,023 183,988 183,937 183,923 Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. and are automatically updated. PREMs are also necessary for management as they provide a patient perspective and align with one of the main goals of PHC: patient centeredness. However, they take more time to collect and depend on response rates, which vary with patient and area characteristics. 1 Their convergence with registered process measures is particularly interesting for health system administrators as these can ensure that process measures reflect aspects that matter to patients. When convergence does not occur, as in the case with the index to measure quality of assistance in the Catalan system, it likely denotes that the two types of measures are too distinct. In our case, the index includes a large number of very different conditions, including diagnostic and resolution items, which makes it unlikely to converge with average patient experience. Breaking down the index and asking patients about the diagnosis and treatment of specific diseases might help establish a stronger association between these types of measures. The results largely confirmed previous studies' evidence about the how sociodemographic factors affect PREMs. The result for age was expected, as extant research shows that older people tend to be more benevolent in their judgment of public services, particularly when the services are perceived to be more important in their daily life and they do not pay for them. 35–38,44 The finding that female patients assess care experience more negatively than men is intriguing and deserves further research. 44 Women tend be more inclined to support public services, 45 but in the case of health services, their evaluations can be influenced by other factors. According to a recent survey, women are more likely than men to say a healthcare provider has dismissed their concerns or did not believe them or to have a negative experience because of their age, gender, or ethnicity 46. We also found that satisfaction with care increased with education. 38 A higher level of education implies a better understanding of the system, which can be associated with a better experience of care. Regarding self-reported health status, better health was found to be associated with a more positive evaluation of the experience of care. 35,38,41 Results for education and self-reported health are in keeping with previous studies, although the evidence for these two measures is inconsistent within the literature. 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1782 BALLART and RICO Patient experience is also affected by contextual characteristics of the area where patients live and where PHC centers are located. In this study, patients living in less deprived areas were found to be more demanding when assessing their patient experience. The opposite was found in a Swedish study where higher social deprivation was associated with lower patient satisfaction. 2 In the Catalan context, residents from more affluent areas are more likely to combine public and private healthcare and may thus be more stringent in their judgments of the public services they receive. 5 | LIMITATIONS One of the current study's limitations is the use of data from one specific region of Spain where the public health system is highly decentralized. Primary healthcare is structured around primary health centers staffed with public personnel (and a few exceptions of private or nonprofit providers). In organizations with public personnel, it may be easier to impose performance targets. Similar European health systems rely more heavily on private doctors for PHC. It is therefore possible that our results will differ from those for other health systems. Another limitation concerns the registered process measures used in our study. The analysis was based on two indexes that are specific to the Catalan administration even if they are similar to the ones used in other European countries. A parallel argument can be made regarding the socio-economic index used to provide contextual data about the health areas where primary health centers are located, which is also specific of the observed system. 6 | CONCLUSIONS This study provides further evidence supporting the claim that registered process measures and PREMs at the primary healthcare level are not entirely separate worlds. Registered process measures reflect system demands just as PREMs have an inherently subjective dimension that is highly dependent on personal characteristics and previous experiences with health care services. However, there are areas (including continuity and accessibility of care) where objective and subjective measures are conceptually similar to each other; hence demands from management appear to align with patient expectations, reflected in their observations of the health care they receive. In other, more conceptually distant dimensions, setting goals and targets to create incentives for doctors to act according to prede- fined care processes have the potential to improve the quality of assistance and prescriptions but are not as clearly reflected in PREMs. For these types of measures to influence patient experience, the explanations given by doctors to patients concerning both procedures and prescriptions are probably a key factor that requires more research. For surveys to reflect the guidelines that the system establishes for professionals, they need to change their focus and collect more detailed information about the diagnosis and treatment of specific conditions. ACKNOWLE DG E ME NTS This work was supported by a grant of the Spanish Ministry of Economy and Competitiveness, grant number: CSO2017-86653-P DATA AVAI LABI LI TY STATE M E N T The survey data that support the findings of this study is publicly available. Data on registered process measures may be requested to the authors but is subject to the authorization of ICS (Institut Català de la Salut) who allowed its use for this study. 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1783 ET HICS STATE ME NT Not applicable. O RC ID Xavier Ballart https://orcid.org/0000-0002-8256-6512 R EF ERE NCE S 1. Glenngård AH, Anell A. Process measures or patient reported experience measures (PREMs) for comparing perfor- mance across providers? A study of measures related to access and continuity in Swedish primary care. Prim Health Care Res Dev. 2018;19(01):23-32. https://doi.org/10.1017/s1463423617000457 2. Glenngård AH, Anell A. Does increased standardisation in health care mean less responsiveness towards individual patients’ expectations? A register-based study in Swedish primary care. SAGE Open Med. 2017;5:2050312117704862. https://doi.org/10.1177/2050312117704862 3. Smith LM, Anderson WL, Lines LM, et al. Patient experience and process measures of quality of care at home health agencies: factors associated with high performance. Home Health Care Serv Q. 2017;36(1):29-45. https://doi.org/10.10 80/01621424.2017.1320698 4. Anhang Price R, Elliott M, Zaslavsky A, et al. Examining the role of patient experience surveys in measuring health care quality. Med Care Res Rev. 2014;71(5):522-554. https://doi.org/10.1177/1077558714541480 5. Aysola J, Xu C, Huo H, Werner R. The relationships between patient experience and quality and utilization of primary care services. J Patient Exp. 2020;7(6):1678-1684. https://doi.org/10.1177/2374373520924190 6. Ortiz G. Examining patients’ perceptions of care to identify opportunities for quality improvement in psychiatric inpa- tient hospitals. Patient. 2014;7(3):301-312. https://doi.org/10.1007/s40271-014-0052-5 7. Orozco-Beltrán D, de Toro J, Galindo MJ, et al. Healthcare experience and their relationship with demographic, disease and healthcare-related variables: a cross-sectional survey of patients with chronic diseases using the IEXPAC scale. Patient. 2019;12(3):307-317. https://doi.org/10.1007/s40271-018-0345-1 8. Jha A, Orav E, Zheng J, Epstein A. Patients’ perception of hospital care in the United States. N Engl J Med. 2008;359(18):1921-1931. https://doi.org/10.1056/nejmsa0804116 9. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpa- tient care and hospital readmission within 30 days. Am J Manag Care. 2011;171(1):41-48. 10. Kennedy GD, Tevis SE, Kent KC. Is there a relationship between patient satisfaction and favorable outcomes? Ann Surg. 2014;260(4):592-598. https://doi.org/10.1097/sla.0000000000000932 11. Llanwarne NR, Abel GA, Elliott MN, et al. Relationship between clinical quality and patient experience: analysis of data from the English quality and outcomes framework and the national GP patient survey. Ann Fam Med. 2013;11(5):467- 472. https://doi.org/10.1370/afm.1514 12. Sacks GD, Lawson EH, Dawes AJ, et al. Relationship between hospital performance on a patient satisfaction survey and surgical quality. JAMA Surgery. 2015;150(9):858-864. https://doi.org/10.1001/jamasurg.2015.1108 13. Tsai TC, Orav EJ, Jha AK. Patient satisfaction and quality of surgical care in US hospitals. Ann Surg [Internet]. 2015;261(1):261-268. https://doi.org/10.1097/sla.0000000000000765 14. Cheon O, Song M, McCrea AM, Meier KJ. Health care in America: the relationship between subjective and objective assessments of hospitals. Int Publ Manag J. 2021;24(5):596-622. https://doi.org/10.1080/10967494.2018.1553808 15. Isaac T, Zaslavsky A, Cleary P, Landon B. The relationship between patients’ perception of care and measures of hospital quality and safety. Health Serv Res. 2010;45(4):1024-1040. https://doi.org/10.1111/j.1475-6773.2010.01122.x 16. Chang JT, Hays RD, Shekelle PG, et al. Patients’ global ratings of their health care are not associated with the Technical qual- ity of their care. Ann Intern Med. 2006;144(9):665-672. https://doi.org/10.7326/0003-4819-144-9-200605020-00010 17. Mahlknecht A, Abuzahra ME, Piccoliori G, Engl A, Sönnichsen A. Are quality promotion initiatives in Austrian and Italian general practices associated with higher patient satisfaction and quality of life? Results from the interventional study ‘IQuaB. Health Soc Care Community. 2022;30(2):e397-409. https://doi.org/10.1111/hsc.13212 18. Sheaff R, Pickard S, Smith K. Public service responsiveness to users’ demands and needs: theory, practice and primary healthcare in England. Publ Adm. 2002;80(3):435-452. https://doi.org/10.1111/1467-9299.00312 19. Adler R, Vasiliadis A, Bickell N. The relationship between continuity and patient satisfaction: a systematic review. Fam Pract. 2010;27(2):171-178. https://doi.org/10.1093/fampra/cmp099 20. Glickman SW, Boulding W, Manary M, et al. Patient satisfaction and its relationship with clinical qual- ity and inpatient mortality in acute myocardial infarction. Circ Cardiovasc Qual Outcome. 2010;3(2):188-195. https://doi.org/10.1161/circoutcomes.109.900597 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1784 BALLART and RICO 21. Brousseau DC, Bergholte J, Gorelick MH. The effect of prior interactions with a primary care provider on nonur- gent pediatric emergency department use. Arch Pediatr Adolesc Med. 2004;158(1):78-82. https://doi.org/10.1001/ archpedi.158.1.78 22. Fenton JJ, Jerant AF, Bertakis KD, Franks P. The cost of satisfaction: a national study of patient satisfaction, health care utilization, expenditures, and mortality. Arch Intern Med. 2012;172(5):405-411. https://doi.org/10.1001/ archinternmed.2011.1662 23. Sequist TD, Schneider EC, Anastario M, et al. Quality monitoring of physicians: linking patients’ experiences of care to clinical quality and outcomes. J Gen Intern Med. 2008;23(11):1784-1790. https://doi.org/10.1007/s11606-008-0760-4 24. Glenngård AH. Productivity and patient satisfaction in primary care—conflicting or compatible goals? Health Pol. 2013;111(2):157-165. https://doi.org/10.1016/j.healthpol.2013.04.012 25. Glenngård AH. Is patient satisfaction in primary care dependent on structural and organizational characteristics among providers? Findings based on data from the national patient survey in Sweden. Health Econ Pol Law. 2013;8(3):317-333. https://doi.org/10.1017/s1744133112000333 26. Santos R, Gravelle H, Propper C. Does quality affect patients’ choice of doctor? Evidence from England. Econ J. 2017;127(600):445-494. https://doi.org/10.1111/ecoj.12282 27. Richter JP, Downs L, Beauvais B, et al. Does the proportion of same-day and 24-hour appointments impact patient satisfaction? [Internet]. Qual Manag Health Care. 2017;26(1):26-28. https://doi.org/10.1097/qmh.0000000000000121 28. Fan V, Burman M, Mcdonell M, Fihn S. Continuity of care and other determinants of patient satisfaction with primary care. J Gen Intern Med. 2005;20(3):226-233. https://doi.org/10.1111/j.1525-1497.2005.40135.x 29. Departament de Salut. La veu de la ciutadania - 2014 [Internet]. Generalitat de Catalunya; 2015. https://scientiasalut. gencat.cat/handle/11351/2885 30. Departament de Salut. PLAENSA: Atenció Primària - 2018 [Internet]. Generalitat de Catalunya; 2019. https://scien- tiasalut.gencat.cat/handle/11351/5522 31. Manary MP, Boulding W, Staelin R, Glickman SW. The patient experience and health outcomes. N Engl J Med. 2013;368:201-203. https://doi.org/10.1056/nejmp1211775 32. Coma E, Ferran M, Méndez L, Iglesias B, Fina F, Medina M. Creation of a synthetic indicator of quality of care as a clinical management standard in primary care. SpringerPlus. 2013;2(1):51. https://doi.org/10.1186/2193-1801-2-51 33. Catalán A, Nin E, Morera E. La incentivación económica de la calidad de la prescripción: estrategia de contención sostenida del gasto farmacéutico en el ámbito de la atención primaria. 4o edición. Premio Profesor Barea; 2005. 34. Salisbury C, Wallace M, Montgomery A. Patients’ experience and satisfaction in primary care: secondary analysis using multilevel modelling. BMJ Clin Res ed). 2010;341(oct12 1):c5004. https://doi.org/10.1136/bmj.c5004 35. Cohen G. Age and health status in a patient satisfaction survey. Soc Sci Med. 1996;42(7):1085-1093. https://doi. org/10.1016/0277-9536(95)00315-0 36. Rahmqvist M. Patient satisfaction in relation to age, health status and other background factors: a model for compari- sons of care units. Int J Qual Health Care. 2001;13(5):385-390. https://doi.org/10.1093/intqhc/13.5.385 37. Kim ES, Moored KD, Giasson HL, Smith J. Satisfaction with aging and use of preventive health services. Prev Med. 2014;69:176-180. https://doi.org/10.1016/j.ypmed.2014.09.008 38. Thornton RD, Nurse N, Snavely L, Hackett-Zahler S, Frank K, DiTomasso RA. Influences on patient satisfaction in health- care centers: a semi-quantitative study over 5 years. BMC Health Serv Res. 2017;17(1):361. https://doi.org/10.1186/ s12913-017-2307-z 39. Chen Q, Beal EW, Okunrintemi V, et al. The association between patient satisfaction and patient-reported health outcomes. J Patient Exp. 2019;6(3):201-209. https://doi.org/10.1177/2374373518795414 40. James O. Evaluating the expectations disconfirmation and expectations anchoring approaches to citizen satisfaction with local public services. J Publ Adm Res Theor. 2009;19(1):107-123. https://doi.org/10.1093/jopart/mum034 41. Dahlström C, Nistotskaya M, Tyrberg M. Outsourcing, bureaucratic personnel quality and citizen satisfaction with public services. Publ Adm. 2018;96(1):218-233. https://doi.org/10.1111/padm.12387 42. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. 2nd ed. Sage; 2002. 43. Gelman A. Scaling regression inputs by dividing by two standard deviations. Statistics Med. 2008;27(15):2865-2873. https://doi.org/10.1002/sim.3107 44. Campbell J, Ramsay J, Green J. Age, gender, socioeconomic, and ethnic differences in patients’ assessments of primary health care. Qual Health Care. 2001;10(2):90-95. https://doi.org/10.1136/qhc.10.2.90 45. Esping-Andersen G. The Incomplete Revolution: Adapting to Women’s New Roles. Polity Press. 46. Long M, Frederiksen B, Ranji U, Salganicoff A. Women’s Health Care Utilization and Costs: Find- ings from the 2020 KFF Women’s Health Survey; 2021. https://www.kff.org/report-section/ womens-health-care-utilization-and-costs-findings-from-the-2020-kff-womens-health-survey-methodology/ 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1785 AUT HOR BI OGRAPHI E S Xavier Ballart is professor of Political and Administrative Sciences at Universitat Autonoma, Barcelona, Spain. He has worked on program evaluation, public policy and public management. In public management, he studies primary health care and performance. Guillem Rico is Associate Professor of Political and Administrative Sciences at Universitat Autonoma, Barcelona, Spain. He is specialized in social science methods and he has worked on various public policy domains. How to cite this article: Ballart X, Rico G. How much convergence exists between registered process measures and patient reported experience measures? A study on Catalan primary healthcare. Int J Health Plann Mgmt. 2023;38(6):1772-1788. https://doi.org/10.1002/hpm.3701 AP P ENDI X TA B L E A 1 Experience of care dimensions and items from the survey. Experience of care dimensions 3,23,34 Survey item Response scale (5-point) Accessibility Got an appointment as soon Q2. How easy to arrange a visit “Excellent” to “very poor” as you needed it on a date that suits you Got an answer when you Q18. How was the attention “Excellent” to “very poor” had a question when you had an urgent matter Time the doctor came in the Q3. Punctuality to enter in the “Excellent” to “very poor” exam room room for the visit with the doctor Interaction-communication Spend enough time Q6. Time the doctor spends “Excellent” to “very poor” on you Explain things Q9. The doctor provides you the “Always” to “never” information you need Q10. You understand the “Always” to “never” information Listen carefully, got an Q7. Predisposition to listen “Excellent” to “very poor” answer when you had a and to take charge of what question worries you Q8. Predisposition to wait for “Excellent” to “very poor” your opinion Q13. Nurse's predisposition to “Excellent” to “very poor” listen and take charge Clinical team interaction Q16. The information provided “Always” to “never” by different professionals is coherent Q17. When other doctors visit “Always” to “never” you, they have information about what they did to you before (Continues) 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1786 BALLART and RICO TA B L E A 1 (Continued) Experience of care dimensions 3,23,34 Survey item Response scale (5-point) Treatment –Support Treat you with courtesy Q12. Treatment (doctor) “Excellent” to “very poor” Q14. Treatment (nurse) “Excellent” to “very poor” Q19. Treatment, kindness “Excellent” to “very poor” (reception personnel) Professionals' caring and Q11. Feeling to be in good “Always” to “never” concern for you hands (doctor) Q13. Feeling to be in good “Always” to “never” hands (nurse) Q20. The visit(s) allowed you to “Excellent” to “very poor” take charge of your health Premises Q5. Cleanness “Excellent” to “very poor” Q4. Noise in the waiting room “None” to “too much” Note: The survey data and documentation are openly available at https://ceo.gencat.cat/es/estudis/registre-estudis- dopinio/estudis-de-la-generalitat/detall/index.html?id=7091. TA B L E A 2 Quality of assistance index. EQAG01 - Cardiovascular disease EQAG08 - Preventive activities - EQA0201 - Adequate treatment of atrial fibrillation - EQA0301 - Alcohol consumption screening - EQA0202 - Good control of treatment with anticoagulants - EQA0302 - Reduction of alcohol consumption in - EQA0203 - Antiaggregating treatment in IC/CVA high-risk drinkers - EQA0204 - LDL control in IC/CVA - EQA0303 - Weight reduction in obesity and - EQA0205 - AT control in IC/CVA overweight - EQA0206 - Treatment with beta-blockers of IC/hart failure - EQA0304 - Abstainers in a population at risk - EQA0207 - Treatment with ACEI or ARAII in CHF and in - EQA0305 - Smoking cessation hypertension or DM with nephropathy (two sub-indicators) - EQA0306 - Improvement of physical activity in - EQD0238 - Diagnostic adequacy of cardiovascular disease insufficiently active patients with CVRF (three sub-indicators) - EQD0313 - Diagnostic adequacy of obesity (2 sub-indicators) EQAG02 - Diabetes mellitus type 2 EQAG09—Vaccinations - EQA0208 - DM2: Diabetic foot screening - EQA0308 - Pneumococcal vaccination coverage for - EQA0209 - DM2: HbA1C control over 64 years old - EQA0210 - DM2: Retinopathy screening - EQA0309 - Tetanus vaccination coverage for over - EQA0212 - AT control in DM 39 years old - EQD0239 - Quality of DM2 diagnosis (three sub-indicators) - EQA0310 - HCV: anti-VBH and anti-VAH vaccination - EQA0312 - Triple viral vaccination in adults - EQA0501 - Influenza vaccination in over 59s - EQA0502 - Influenza vaccination risk population EQAG03 - Arterial hypertension EQAG10—ATDOM (attention at home) - EQA0213 - AH: AT control - EQA0401 - Comprehensive assessment of people in - EQA0235 - AH: AT control in patients with CRF ATDOM - EQD0240 - Diagnostic adequacy of hypertension - EQA0402 - Assessment of the risk of pressure ulcers in ATDOM population - EQA0403 - Achieving a safe environment at home in ATDOM population - EQA0404 - Caregiver overload of ATDOM patients 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BALLART and RICO 1787 TA B L E A 2 (Continued) EQAG04—Dyslipidemia EQAG11 - Social assessment - EQA0214 - LDL control in patients with high CVR - EQA0406 - Social assessment of dependent people - EQA0215 - Dyslipidemia: Calculation of CVR (35-74a) - EQA0407 - Social assessment in frail elderly - EQD0241 - Diagnostic adequacy of hypercholesterolemia EQAG05 - Chronic respiratory obstruction EQAG12 - Quaternary prevention - EQA0220 - Verification of inhalers - EQA0216 - Low CVR with wrongly indicated - EQD0242 - Diagnostic adequacy of respiratory disease (5 lipid-lowering agents sub-indicators) - EQA0217 - New wrongly indicated statins - EQA0222 - Inadequacy of the prevention of gastropathy with PPIs - EQA0224 - Correct treatment of asymptomatic hyperuricemia - EQA0228—Wrongly indicated treatment in osteoporosis with low risk of fracture - EQA0229 - Adequacy of treatment in acute gastroenteritis - EQA0232 - Adequacy of the treatment of acute bronchitis, CVA and flu - EQA0238 - Persistence of wrongly indicated statins - EQA0239 - Incorrect use of PSA in over 70s - EQA0313 - Incorrect use of PSA - EQA0314 - Excessive metabolic control DM2 in an aging population EQAG06 - acute pathology EQAG19—Dentistry - EQA0223 - Correct treatment of nephritic colic - EQA0601 - Oral examination in diabetic patients - EQA0226 - Adequacy of treatment in acute tonsillitis - EQA0602 - Oral examination in DM2 with poor - EQA0230 - Appropriate treatment of urinary tract infections glycemic control - EQA0231 - Adequacy of the treatment of non-suppurative acute otitis EQAG07 - other health problems - EQA0219 - Monitoring of new cases of iron deficiency anemia - EQA0227 - Good control of hypothyroidism - EQD0247 - diagnostic adequacy of other health problems (8 sub-indicators) Note: Restrictions apply to the availability of the raw data, which were used under license for this study. Data are available from the authors with the permission of Catalan Health Institute. The classification of indicators is available at http:// pressupostos.camfic.cat/DemoECAP/umi_1/sisap-umi.eines.portalics/index.html. TA B L E A 3 Quality of prescription index. Use of antibiotics (AB) Use of drugs for respiratory pathology (asthma and COPD) - IF419 - DHD of antibiotics - IF427 - % recommended drugs/asthma and COPD - IF421 - % Amoxicillin + clavulanic/total penicillin - IF423 - % recommended drugs/AB Use of urinary antispasmodics Use of medicines for osteoporosis - IF447 - DHD urinary antispasmodics - IF415 - DHD Medicines osteoporosis - IF449 - % recommended drugs/urinary antispasmodics - IF417 - % recommended drugs/total osteoporosis drugs Use of hypnotic anxiolytics (ANSH) Use of medicines for musculoskeletal pathology - IF435 - DHD of hypnotic anxiolytics (HANS) - IF409 - DHD nonsteroidal anti-inflammatory drugs - IF437 - % recommended drugs/HANS (NSAIDs) - IF411 - % recommended drugs/NSAIDs - IF413 - DHD Chondroprotectors (Continues) 10991751, 2023, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hpm.3701 by Readcube (Labtiva Inc.), Wiley Online Library on [30/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 1788 BALLART and RICO TA B L E A 3 (Continued) Use of antidepressants (DEPRE) Use of antiulcer drugs (ULC) - IF429 - DHD of antidepressants - IF405 - DHD of antiulcer drugs - IF431 - % recommended drugs/DEPRE - IF407 - % PPI recommended/total PPI - IF433 - % recommended second-line drugs Use of antidiabetics Use of antihypertensives (AHT) - IF439 - % alternative antidiabetics to SULFONYLUREES - IF399 - % IUD alone or associated with recommended/ RECOM/DIABO AHT - IF441 - % recommended drugs/oral antidiabetics - IF401 - % ARAII/(IECA + ARAII) - IF443 - % Recommended basal insulins - IF403 - % recommended drugs/AHT Use of drugs for Benign Prostatic Hyperplasia (BPH) Global indicators - IF445 - % recommended drugs/BPH - IF455 - Follow up PHF recommendations - APC (annual) - IF457 - Following recommendations of the PHF - APC Use of hypolipidemic agents (HIPO) - IF425 - % recommended drugs/lipid-lowering agents Note: Restrictions apply to the availability of the raw data, which were used under license for this study. Data are available from the authors with the permission of Catalan Health Institute. The classification of indicators is available at http:// pressupostos.camfic.cat/DemoECAP/umi_1/sisap-umi.eines.portalics/index.html.