In care improvement, despite multiple studies on patient-reported experiences, expectations and satisfaction in care, 21–27 only a few studies have involved patients in identifying which components of the care system needed to be improved and how, and these studies often addressed specific issues, in given conditions or contexts. 28 29 In the present study, we aimed to engage patients with chronic conditions, on a large scale, to identify which components of their care require improvement and to generate ideas to do so.

Transforming healthcare is difficult because all parts of the system are intricate and influenced by political, economic and cultural factors and are constrained by value conflicts and resistance to change. 16 17 In 2013, an international movement called the ‘Patient Revolution’ was formed to enlist patients, who live and experience the healthcare system every day, to help in designing care services better suited to them. 18 The Patient Revolution mirrors initiatives launched in other fields that rely on citizen science methods and the collective intelligence of large groups of people to tackle complex problems. For example, the project Collective Intelligence for Public Transport in European Cities gathered ideas provided by 500 citizens to invent cleaner and better transport in cities 19 and the Climate CoLab gathered propositions to tackle climate change from more than 90 000 people all over the world, over 7 years. 20

Chronic disease is a global epidemic. 1 In western countries, 40%–60% of adults have a chronic condition; 50% of these adults have multiple conditions and are considered multimorbid. 2 3 This epidemic of chronic conditions challenges disease-centric care models in both European and American settings. 4 Despite recent improvements, the care for patients with chronic conditions, in France and in other western countries, remains fragmented and uncoordinated, with a risk of polypharmacy and harmful interactions. 5 6 This situation generates an important burden of treatment for patients that can affect their lives as much—or more—than the diseases themselves. 7 8 In addition, in France, the care for patients with chronic conditions is affected by the decreasing medical coverage of large parts of the French territory because of a cost containment-driven policy to limit the number of professionals trained in medical schools and the progressive closure of local care structures for the benefit of larger urban medical centres. 9 As a result, health professionals are asked to do more in less time and patients are hurried along in their consultations. 10–12 Worse, initiatives to improve healthcare may have paradoxically contributed to the emergence of myriad norms, standardised protocols and pay-for-performance regulations, which hinder the importance of human relationships in care and contribute to a feeling of cumulative bureaucracy. 13–15

Methods

We performed a citizen science study with two steps. In the first step, patients with chronic conditions provided ideas on aspects of their care they wanted changed by using an online questionnaire with a broad open-ended question. Then, in a second step, another sample of participants reflected on and enriched the ideas identified.

Setting and participants This study was nested within the Community of Patients for Research (ComPaRe), an ongoing citizen science project based on an e-cohort of patients with chronic conditions (www.compare.aphp.fr). Participants are adults (>18 years old) who report having at least one chronic condition (defined as a condition requiring healthcare for at least 6 months). ComPaRe involves mostly patients in France, although some participants may be from French-speaking countries (Belgium, Switzerland, and so on). Patients join the project to donate time to accelerate research on their conditions. They can do this by answering regular patient-reported outcomes and patient-reported experience instruments, suggesting ideas for new research or participating in the set-up or analysis of research projects. The recruitment started in January 2017 and is still ongoing, with about 18 000 patients included in November 2018. All participants provide electronic consent before participating in the e-cohort.

First step: the ‘magic wand’ question In a first step, we analysed, in January 2018, data from a specific online questionnaire inviting patients to suggest ideas to improve their healthcare. The questionnaire was sent to all participants enrolled in ComPaRe (ie, adults with at least one chronic condition) starting from 9 May 2017. A single open-ended question was used to capture patients’ ideas to improve their care: ‘If you had a magic wand, what would you change in your healthcare?’ This open-ended question was inspired by (1) the ‘Miracle’ question used in solution-focused therapy to encourage participants to focus on possibilities rather than problems,29 and (2) a previous work aimed at identifying the propositions of patients with HIV to reduce their burden of treatment in sub-Saharan Africa.30 The magic wand question was pilot-tested by six patients from ComPaRe to ensure its clarity. Because testing took place before the development of the questionnaire, answers during the pilot phase were not merged with research data. Open-ended answers were analysed by using thematic analysis with multiple rounds of analysis. In a first step, two investigators (VTT and CR) read the first 180 responses and independently identified ‘ideas’: literal sentences used by participants to describe their ideas to improve healthcare. Coding was limited to explicit statements of potential modifications in healthcare. For example, changes related to patients’ conditions or symptoms (eg, ‘If I had a magic wand, I would like to cough less’) or wishes for more research (eg, ‘I would like more research on my condition so that new effective treatments could be developed’) were dropped from the analysis. Patients’ statements of barriers and burdens in healthcare were extrapolated into wishes for improved care (‘There are too many visits’ was coded as ‘I wish there were fewer visits’). During regular meetings, the two investigators reached consensus on the ideas identified and grouped them into ‘areas for improvement’ according to the context, people and processes involved to implement these ideas. In a second step, this initial set of areas for improvement was used to classify the remaining responses. Each participant’s response was read by the two investigators, who independently assigned data segments to each area for improvement. During frequent meetings, the investigators compared their analyses and reached consensus on coding. This second phase involved more than simply sorting data segments: whenever new ideas emerged, researchers discussed these ideas and refined and enriched the list of areas for improvement.

Second step: enrichment of findings by patients In April 2018, in a second step, we asked all patients who had answered the ‘magic wand’ question to reflect on and enrich the list of areas for improvement identified in the first step. We invited both people who had participated in the first step (n=1227) and people who had not because they enrolled in ComPaRe after January 2018 (n=741). We invited these participants to answer a second web questionnaire presenting all areas for improvement identified so that they could (1) assess whether they agreed with the list and (2) suggest new ideas that could have come up when reviewing the list. Indeed, reflecting on others’ inputs may enhance creativity and improve the quality of answers.31 32 To ensure that all three levels (consultation, hospital/clinic and health system) would receive similar attention by participants, these levels were presented in a random order to participants. Open-ended data collected during this second step were analysed by two investigators (VTT and CR) using the same methodology as during the first step. Investigators extracted all novel patients’ ideas to improve their care and during meetings decided how these ideas could be classified under existing areas for improvement or could define new areas for improvement. Then, another investigator (PR) and two patients (AC and CP) triangulated all findings from both the first and second steps of the study. During recorded meetings with the main investigator (VTT), they went back to the raw data and independently recoded participants’ answers (the whole corpus was recoded by PR and 10% of the corpus was recoded by the two patients) to challenge both the list of areas for improvement and how ideas had been classified. Finally, all investigators reached consensus on the classification of all patients’ ideas in areas of improvement.

Assessment of the point of data saturation The objective of our study was to identify and list ideas for improvement in the care of patients with chronic conditions, from patients’ perspective. To ascertain that we achieved an exhaustive description of these ideas, we assessed whether data saturation had been reached. Data saturation represents the point in data collection and analysis when new information produces little or no change to the codebook, the codebook representing the collection of codes that link expressions found in text to all abstract constructs identified by the researchers.33 To determine the point of data saturation, we used a mathematical model to predict the potential number of new ideas that could be identified by adding new participants in the study.34 This model involved (1) drawing the ‘observed’ theme accumulation curve (eg, number of different areas for improvement identified during the course of the study); (2) predicting the theoretical number of areas for improvement that could be found with the inclusion of more patients; and (3) estimating the local slope of the expected theme accumulation curve (ie, the number of patients to be included to identify a new theme). Thus, the model did not inform on the nature (‘quality’) of themes identified but rather on the probability to find new themes by recruiting new participants. We examined the number of additional areas of improvement that could have been found by including more participants globally and in different subgroups defined by sex, age (≤50 and>50 years old), multimorbidity and educational level (college vs lower education). Analyses involved use of R V.3.3 (http://www.R-project.org, the R Foundation for Statistical Computing, Vienna, Austria).