Luiza Siqueira

Do Prado

PhD Samuel Allemann

Dr Marie Viprey

PharmD, PhD; Anne-Marie Schott

Dr Dan Dediu

PhD Alexandra Lelia Dima

MSc Luiza Siqueira Do Prado

Toward an Interdisciplinary Approach to Constructing Care Delivery Pathways From Electronic Health Care Databases to Support Integrated Care in Chronic Conditions: Systematic Review of Quantification and Visualization Methods

Keywords: electronic health care databases, patient pathway, data visualization, systematic review

published or not. The documents may come

Introduction

Background

Secondary use of patient data recorded during health care delivery in electronic health care databases (EHDs) has the potential to improve health care quality and reduce costs [START_REF] Meystre | Clinical data reuse or secondary use: current status and potential future progress[END_REF]. As long-term care consists of interactions with many health care providers for a long period [START_REF] Bodenheimer | Improving primary care for patients with chronic illness[END_REF][START_REF] Pham | Care patterns in medicare and their implications for pay for performance[END_REF], EHD data can be particularly useful for supporting decisions related to improving long-term care delivery [START_REF] Meystre | Clinical data reuse or secondary use: current status and potential future progress[END_REF]. With the increasing prevalence of chronic conditions worldwide, health care organizations can benefit from methods to link and transform EHD data from multiple sources into comprehensive descriptions of patients' recent health status and health care use history [START_REF] Samal | Leveraging electronic health records to support chronic disease management: the need for temporal data views[END_REF][START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF][START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF]. These descriptions, which we refer to as data-driven care delivery pathways (CDPs), may apply numeric (quantification) or graphical (visualization) methods to synthesize information on the often-fragmented patients' health care journeys [START_REF] Siqueira Do Prado | Quantification and visualisation methods of data-driven chronic care delivery pathways: protocol for a systematic review and content analysis[END_REF]. The aim of CDPs is either to provide relevant clinical and contextual information to assist health care professionals (HCPs) and patients in making shared decisions on the course of treatment or to investigate sources of variation in health care use at the organization or system levels to inform quality improvement decisions. Providing feedback from routine care delivery via CDPs shows promise in reducing fragmentation and improving decision-making in chronic disease management [START_REF] Samal | Leveraging electronic health records to support chronic disease management: the need for temporal data views[END_REF]. CDPs are obtained from patients' electronic records and retrospective evaluation of the CDPs with patients, in relation to their care goals and experiences, may help assess and work toward improving person-centered integrated care in long-term conditions [START_REF] Berntsen | A person-centered integrated care quality framework, based on a qualitative study of patients' evaluation of care in light of chronic care ideals[END_REF].

Initial efforts to build such descriptions in different settings highlight the many challenges of developing CDPs that support clinical care in meaningful, reliable, and actionable ways. The challenges can be grouped into 3 domains: clinical, technological, and behavioral. First, the complexity of clinical situations may require information on multiple parameters relevant to a diverse range of decisions in the care process. CDPs require careful selection of key information depending on evidence-based clinical processes and treatment options, as well as data availability [START_REF] Yang | Process mining for clinical pathway: literature review and future directions[END_REF][START_REF] Kinsman | What is a clinical pathway? Development of a definition to inform the debate[END_REF]. Second, developing the technology to access, link, clean, and produce comprehensible descriptions of these data and make them available at the point of care is a complex task. CDP visualization and quantification methods need to meet standards of data quality criteria of completeness, consistency, accuracy, reliability, and timeliness, among others [START_REF] Liaw | Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature[END_REF]. Third, the aims of these methods can only be reached if their intended users act on this information in ways that optimize patients' interactions with their HCPs and the decisions taken. Thus, CDP visualization and quantification methods, as with all complex interventions targeting human behaviors, should be designed to facilitate concrete actions by individuals (in this case, patients and HCPs) in specific contexts and moments in time [START_REF] Presseau | Action, actor, context, target, time (AACTT): a framework for specifying behaviour[END_REF]. Although these 3 domains have been partially considered in published work, no agreed approaches exist to deal with all the clinical, technical, and behavioral aspects of developing, evaluating, and implementing data-driven CDP visualization and quantification methods in long-term care. As health systems embark on similar projects that access data from EHD to guide the optimization of long-term care services, they would benefit from the learnings accumulated from the methods developed in recent years and how they considered these 3 domains. The insights gained could represent a basis for specifying minimal procedures to follow in project planning and conducting and reporting future projects and thus ensure more streamlined evidence synthesis in this field.

Objective

The objective of this review was to describe and synthesize the different characteristics of quantification and visualization methods of data-driven long-term CDPs published in the scientific literature. We aimed to answer the following research questions:

1. What clinical information does the method use and how was it considered relevant? 2. What are the method's development and implementation characteristics? 3. Which behaviors and interactions does the method aim to promote among users and how?

Methods

Overview

The protocol for this review was registered in PROSPERO (CRD42019140494) and published [START_REF] Siqueira Do Prado | Quantification and visualisation methods of data-driven chronic care delivery pathways: protocol for a systematic review and content analysis[END_REF]. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; see the checklist in Multimedia Appendix 1 [START_REF] Page | The PRISMA 2020 statement: an updated guideline for reporting systematic reviews[END_REF]) guidelines [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF] and included 7 steps: literature search, record screening and preliminary selection (title and abstract), full-text screening and final selection, data extraction, deductive-inductive analysis, critical appraisal, and data synthesis. In addition to the criteria mentioned in the protocol (involvement of stakeholders, source of funding, and conflicts of interest), critical appraisal was performed using the Quality Assessment Tool for Reviewing Studies With Diverse Designs (QATSDD) [START_REF] Sirriyeh | Reviewing studies with diverse designs: the development and evaluation of a new tool[END_REF], a 16-item quality assessment tool designed to be applied to quantitative, qualitative, or mixed methods studies. The items received a score ranging from 0 (no information) to 3 (complete explanation). We attributed a "not applicable" (N/A) label when appropriate. We calculated the total score, ranging from 0 to 1, as the sum of the scores for applicable items divided by the number of applicable items. No other modifications were made to the initial protocol.

A literature search was performed using the PubMed (MEDLINE), Scopus, IEEE, CINAHL, and Embase databases. The terms searched were related to 3 topics: "data-driven" (Medical Subject Headings [MeSH] terms such as "electronic health record" and "data mining"); "clinical pathways" (MeSH terms such as "clinical pathway" and "disease management"); and "chronic conditions" (MeSH term "chronic diseases"). The search strategy is available in Multimedia Appendix 2. We considered peer-reviewed publications that (1) reported methods for visualization or quantification of data-driven chronic CDPs (including protocols and reports of study results; see the definition in Figure 1), (2) used data from people living with chronic conditions (ie, needing medical care for >12 mo), and

(3) were published in English. No restrictions on publication date (up to March 2022), study design, population characteristics, type of health care facility, or level of care were applied. The following exclusion criteria were applied: studies that (1) aimed only to assess health care use over a specific period as part of a single research study, for example, as an outcome to evaluate health-related interventions, to describe populations or disease prevalence or as a proxy measure of disease aggravation risk; (2) did not mention population or data characteristics; (3) did not state that they analyzed data from people living with chronic conditions; (4) did not have full texts available; and (5) were not available in English. In addition, conference abstracts or abstract-only papers, systematic or narrative reviews, meta-analyses, and gray literature were not considered in the review.

Figure 1. Definition of quantification and visualization methods of data-driven long-term care delivery pathways (CDPs) and illustration of a hypothetical example of a patient: In the time window selected for CDP construction, several health care use events occurred, including primary care visits, medication prescribing and dispensing, and hospitalization, and were recorded in 3 separate databases. For informing decisions in clinical encounters or at the organizational or policy level, this information needs to be extracted, linked across data sources, prepared for analysis, and results displayed in a user interface available in the intended situations. EHD: electronic health care database.

Record screening was performed using the web-based systematic review management software Covidence (Veritas Health Innovation). After removal of duplicate studies, titles and abstracts were screened independently by 2 raters, as were full-text reports. Disagreements were discussed with a third rater until consensus was reached. We did not assess the interrater reliability for screening. We checked the reference lists for additional relevant studies.

Data Extraction

An electronic data extraction form was used to extract information from included reports on study characteristics (authors, title, type of study, year and country of study, objective, and research questions) and population characteristics (number of patients, age, gender, chronic conditions). Moreover, for the clinical domain, we extracted information on clinical and cost outcomes and the clinical information presented on the proposed interfaces, if present, or data summaries, along with the description of how authors evaluated the relevance of the information (eg, consulting with experts). We also extracted data on method development and validation and stated users and use case scenarios. Data extraction was performed by a single rater and reviewed by a second rater, whereas quality assessment was performed by 2 raters, independently.

In the technological domain, we performed the deductive-inductive content analysis to appraise method development and validation. Inductive analysis includes open coding and creating categories directly from the analyzed text, whereas deductive analysis uses existing data applied to a new context [START_REF] Elo | The qualitative content analysis process[END_REF]. To perform the deductive analysis, we used the framework proposed by Moreno-Conde et al [START_REF] Moreno-Conde | Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis[END_REF] to describe projects defining clinical information models (CIMs). CIMs are technical specifications that define how information is organized and described within electronic health record (EHR) systems, thus facilitating data entry, storage, exchange, analysis, and display. CIMs are developed based on standard reference models and clinical terminologies and work toward a locally implementable structure and semantics that are consistent with these standards, thus enabling interoperability. The description of technological development included 7 steps: scope definition, domain analysis, tool design, definition of tool specifications, validation, publishing and maintenance, and governance [START_REF] Moreno-Conde | Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis[END_REF]. We extracted descriptions of the CDP methods corresponding to these steps. If these descriptions included information that could not be mapped onto these 7 categories, we constructed new categories inductively. The resulting updated framework (described in the Results section) was discussed among 2 coders until consensus was reached.

In the behavioral domain, considering the use of health information systems such as hospital information systems (HISs) and other clinical software as health-related processes, we applied the Action, Actor, Context, Target, Time (AACTT) framework to analyze the use scenarios presented and describe user behaviors. AACTT is a behavior specification framework applicable to implementation interventions in health care to clarify the behaviors of stakeholders across multiple levels of the health system. An action is the behavior that needs to change or occur, in terms that can be observed or measured; an actor is the person (or persons) that does or could do the actions targeted; a context is the physical location, emotional context, or social setting in which the action is performed; a target is the person (or persons) with or for whom the action is performed; and time specifies when the action is performed (time, date, or frequency) [START_REF] Presseau | Action, actor, context, target, time (AACTT): a framework for specifying behaviour[END_REF].

Results

Overview

We identified 3331 records across the databases searched, resulting in 2821 records after duplicate removal and 81 records after title and abstract screening. Citation searching led to the identification of 9 records that reached full-text screening. Finally, 14 studies were included from 14 reports (Figure 2 [13]). Of the 14 studies, 10 (71%) were performed in English-speaking countries: the United States [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF], United Kingdom [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], Australia [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF], and New Zealand [START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF]; the rest (n=4, 29%) were performed in Italy [START_REF] Panzarasa | A careflow management system for chronic patients[END_REF], China [START_REF] Guo | Visual progression analysis of event sequence data[END_REF], Finland [START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], and Germany [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. A total of 11 (79%) articles were published after 2011 [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], and 3 (21%) articles were published by the same group [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF]. We identified 1 (7%) protocol [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], 12 (86%) descriptive studies [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], and 1 (7%) validation study [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. All but 1 (7%) study had descriptive objectives, that is, presented the method and its development. Although all studies focused on support systems for long-term care, the objectives were described using different terms from different perspectives: clinical decision support system [START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], decision support system focused on care planning [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF], data aggregation from different sources in the continuum of care [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], care coordination (care flow management) system [START_REF] Panzarasa | A careflow management system for chronic patients[END_REF], linkage system between different data sets [START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF], system to predict health status transitions [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF], framework and ontology for chronic disease management [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF], and systems to build and visualize clinical pathways [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF]. A total of 8 (57%) studies and the protocol reported a method validation process [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF].

In 1 (7%) study, evaluation with patients and clinicians in real-life clinical settings was reported [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. The studies targeted different chronic conditions: type 1 and type 2 diabetes [START_REF] Panzarasa | A careflow management system for chronic patients[END_REF], type 2 diabetes and hypertension [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], ankylosing spondylitis [START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF], glioblastoma multiforme [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF], prostate cancer [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF], traumatic brain injury [START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], chronic kidney disease [START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF], rheumatoid arthritis [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], and hypertension [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF]; 4 (29%) studies targeted patients with multiple chronic conditions [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF]. All data collected from the studies are available in Multimedia Appendix 3 [START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF][START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. The characteristics of the included studies are detailed in Table 1.

Critical Appraisal

Of the 14 studies, 8 (57%) studies involved stakeholders in the development [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. Although 2 (14%) studies declared to have received public and private funding [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF], 4 (29%) did not declare funding [START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], 7 (50%) declared to have received public funding [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], and 1 (7%) declared to have received no funding [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF]. A total of 6 (43%) studies included a conflicts of interest section and declared no interests [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], and 8 (57%) studies did not specify [START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF]. Four QATSDD items were applicable to all or most of the studies: "Statement of aims/objectives in main body of report," "Clear description of research setting," "Evidence of user involvement in design," and "Strengths and limitations critically discussed." The QATSDD appraisal results are available in Multimedia Appendix 4 [START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF][START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. The scores ranged between 0.33 and 1 (mean 0.64, SD 0.16).

Clinical Domain: What Information Was Used and How Was It Considered Relevant?

The most common clinical aim was to provide visualizations of longitudinal health care use data to optimize clinical pathways (Table S1 in Multimedia Appendix 5) [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF]; other aims were care planning [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF], detecting patients with chronic conditions on suboptimal management [START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF], or informing care decision-making [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF]. Half of the studies (7/14, 50%) [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF] used data from multiple EHDs, including EHRs and HISs. The most common relevance criteria for data selection were consultations with experts [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF] and guidelines for the targeted chronic condition [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF]. One study [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF] reported an evaluation in clinical practice with patients and clinicians, and the evaluated outcomes were both clinical (patient-reported outcome measures, eg, functional ability and disease activity) and related to user experience (acceptability, usability, user satisfaction, and clinical relevance of the platform). Some records proposed real-life evaluation criteria for future work, ranging from comparing HCPs' performance with and without the proposed system [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], comparing care maps produced by the system to those produced using traditional process mapping methods [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], to using qualitative methods such as think-aloud and focus groups [START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF]. Although 1 (7%) record reported that the system provided feedback on cost [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF] and 1 (7%) built care pathways using EHR and medication cost data [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF], none reported cost outcomes for evaluation.

Technological Domain: How Were the Methods Developed and Implemented?

Of the 7 categories by Moreno-Conde et al [START_REF] Moreno-Conde | Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis[END_REF], 6 were identified; the exception was publishing and maintenance, which was not reported on by any study. Two new categories were identified through inductive content analysis: dataflow and transformation (explicitly describing how different data sets were linked or the linkage algorithms that were used to trace the final data items, to understand how these tools could be integrated into existing EHRs, and to consider interoperability) and data protection (describing measures taken to protect patients' data, such as anonymizing or pseudoanonymizing patient data to ensure protection of private or sensitive data). These categories included up to 3 subcategories each, resulting in 14 technological characteristics; the 14 studies reported information referring to a median of 7 (IQR 5.5) subcategories, ranging from 3 to 11 (Table 2). All studies (N=14) included information on the domain to be covered and whether the scope of the presented system was geographically local or wider. Although 10 (71%) studies mentioned involving a group of experts or discussing with clinicians during the method development [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], 2 (14%) studies [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF] provided details about how expert feedback would be obtained and applied. In 1 (7%) study, interviews with 11 specialists were conducted to develop the first version, and later an iterative process of feedback and development was undertaken with 5 specialists [START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF]. The study protocol stated that development would be iterative, with a clinical expert and the informatics lead [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF]. Clinical scenarios and workflows were presented in 4 (29%) studies [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF]. The characteristics of the existing systems, such as how they were implemented and documented, were presented in 4 (29%) studies [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF]. Most studies (11/14, 79%) detailed the set of attributes associated with the method [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. Implementable technical specifications were presented in 8 (57%) studies [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. Validation or testing was performed using different strategies: applying the developed tool to a cohort of patients and evaluating key performance indicators (4/14, 29%) [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF], comparing between using the tool and not using the tool (2/14, 14%) [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], adding the system to an existing EHR system and collecting expert feedback through an initial usability test (1/14, 7%) [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF], or through showing the tool to experts and performing qualitative interviews (n=1, 7%) [START_REF] Guo | Visual progression analysis of event sequence data[END_REF]. In 1 (7%) study, a real-life evaluation was performed [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. Another (n=1, 7%) study reported on the evaluation of a platform held by a consortium responsible for developing and maintaining the method [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. A total of 6 (43%) studies explicitly described how different data sets were linked or the linkage algorithms [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF]. Although the architecture or a conceptual model of the system was provided in 5 (36%) studies [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF], system architecture and data models, expressed in Unified Modeling Language, XML, or as ontologies, were reported in 6 (43%) studies [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF]. In addition, 1 (7%) study described and discussed data quality in EHR [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF], and 1 (7%) study mentioned using the Health Level 7 and Fast Healthcare Interoperability Resources standards to enable data exchange with other systems [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF]. In 8 (57%) studies, anonymized [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF] or pseudoanonymized patient data were used [START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF]. In 1 (7%) study, a publicly available anonymized data set was used [START_REF] Guo | Visual progression analysis of event sequence data[END_REF], and in 1 (7%) study, the platform was cloud based following European standards to ensure data security [START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF].

Behavioral Domain: Actions and Interactions to Improve Care Delivery

In total, 3 (21%) studies presented use scenarios with sufficient detail to identify the actor (who would use the method), activities, contexts, moments, and target (Table 3).

Patient

After system alert Patient's home

To modify treatment Physician "An important task performed by the Cf is the automatic evaluation of home monitoring data, as soon as they are sent to the diabetes management center, in order to detect potential critical situations that should be notified to the care providers or that could require further clinical investigations. This analysis calculates several descriptive statistics (i.e., arithmetic means, standard deviations and the highest and lowest values in a given period of time), performs data interpretation through the extraction of patterns of clinical importance and consistency checking. If the Cf notices that the patient is not responding in the expected way to the therapy (e.g., hyperglycemia and hypoglycemia are too frequent) it generates a guideline-based suggestion to the physician about the need of a therapy modification."

Scenario 2

Patient Periodically Patient's home

To schedule medical visit Patient "In order to help physicians and patients in the management of the long-term screening, the CfMS schedules periodic visits based on the patient's care process history"

Mabotuwana and Warren [26] 2010 Scenario 1

Patient When identified Consultation Treats nonadherence or add new treatments GP a "Awareness of immediate cases -identification of those patients that, at a particular moment in time, are out of supply of an indicated medication. In the first instance, the action is to treat the non-adherence as inadvertent and recall the patient and/or simply prescribe as indicated at the next opportunity. This includes not just patients with lapsed medications, but also those whose circumstances have changed (e.g., due to development of a comorbidity) and thus require additions to previous therapy"

Scenario 2

Patient

Not clear Consultation

To engage in "problem-GP "Opportunity for communication with those with poor supply profiles -at some point it becomes logical to look to a lack of solving" approach concordance between doctor and patient, and/or to the ability of the patient to achieve adherence for other reasons. Low Medication Possession Ratio over an extended time period and repeated lapses in medication supply indicate the need for improved communication between GP and patient; possibly the clinician needs to engage the patient more in a joint ''problem-solving" approach in relation to underlying adherence barriers."

Scenario 3

Patient Not identified Not identified

To give feedback to GPs on guideline adherence Not identified "Opportunity to critique GPs on their adherence to established guidelines and compare practices on specified criteria -for example, the JNC7 hypertension guideline recommends ACEi/ARB medication as compellingly indicated therapy for patients with comorbid hypertension and chronic kidney disease. If an agreed set of audit criteria can be established, this form of reporting also provides an opportunity to compare GP practices (as we have done here with two practices) in an attempt to provide feedback to the GPs to improve the management of their patients with chronic conditions."

Warren et al [25] 1999

Scenario 1 User (GPs) "Users are actively alerted to review relevant guidelines through the several mechanisms: 1) Flags on observations. Right-clicking these observations or the flags will invoke the relevant guideline. Relevant guidelines will appear where observations are recorded (such as in the Initial Medical Assessment form) and in the "heads up" patient summary. 2) Flags on services. 3) Explicit save-time warnings. In some specific cases like vaccination the user will be prompted to consider a particular guideline before exiting the client application." a GP: general practitioner.

Other studies mentioned intended uses in general terms (eg, "improve shared decision-making" or "help clinicians in making decisions"). Although 3 (21%) articles did not specify who the intended end users were [START_REF] Sun | Predicting changes in hypertension control using electronic health records from a chronic disease management program[END_REF][START_REF] Husain | HERALD (health economics using routine anonymised linked data)[END_REF][START_REF] Litchfield | Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol[END_REF], 6 (43%) stated that the end users were physicians (primary care providers or specialists) [START_REF] Hsu | Context-based electronic health record: toward patient specific healthcare[END_REF][START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF][START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF][START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF][START_REF] Guo | Visual progression analysis of event sequence data[END_REF][START_REF] Umer | A decision support system for diagnostics and treatment planning in traumatic brain injury[END_REF] and 5 (36%) included patients and families as end users, in addition to physicians and managers [START_REF] Zhang | Data-driven clinical and cost pathways for chronic care delivery[END_REF][START_REF] Zhang | On clinical pathway discovery from electronic health record data[END_REF][START_REF] Zhang | An interactive platform to visualize data-driven clinical pathways for the management of multiple chronic conditions[END_REF][START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF].

Panzarasa et al [START_REF] Panzarasa | A careflow management system for chronic patients[END_REF] mentioned 2 scenarios. The first described a physician using a warning generated by a care flow management system from home monitoring of blood glucose and intervening to modify treatment with the patient, if needed. The second described patients and physicians using suggestions for periodic medical visits issued by the system based on integrated guidelines and health care use data to plan their care. Mabotuwana and Warren [START_REF] Mabotuwana | ChronoMedIt--a computational quality audit framework for better management of patients with chronic conditions[END_REF] described 3 scenarios. In the first, the system detects patients who are out of medication supply and warns the physician to either address nonadherence or provide a prescription in the next consultation. The second described the system as allowing physicians to become aware of patients with whom they might have to improve communication and engage in a "problem-solving" approach, also by detecting patients with low medication availability. In the third scenario, they described the system as providing audit criteria and relevant data to allow for the assessment of physicians' adherence to guidelines and compare practices. Warren et al [START_REF] Warren | Chronic disease coordinated care planning: flexible, task-centered decision support[END_REF] described a scenario in which the system alerts users (physicians) to review guidelines.

Discussion

Principal Findings

This review takes stock of pioneering work on methods to quantify and visualize CDPs and describes the characteristics of the resulting tools and their development from 3 key perspectives: clinical, technological, and behavioral. We identified 14 studies targeting different chronic conditions and clinical settings in 8 countries, indicating the international reach of this emerging area. From a clinical perspective, the main aim and expected benefit of these methods were to improve physicians' decision-making by enhancing the interpretation of individual-level data; to this end, clinical guidelines and collaborating clinicians provided the relevant input for method development. From a technological perspective, most studies presented details on technological development, system architecture, and data sources. Few studies provided information on the validation processes, and only 1 real-life evaluation was performed. Most papers reported stakeholder involvement before or during development. From a behavioral perspective, 3 of the 14 studies mentioned possible actions intended to follow from accessing CDP data or visualizations, referring to adjusting treatment, care planning, supporting treatment adherence, or clinicians' guideline adherence. All but one of the studies were descriptive reports of early development work and described similar development steps. Nevertheless, the substantial variation in the types of information reported suggests a need for structuring common methodological standards for guiding future projects and facilitating evidence synthesis.

From a clinical perspective, the reviewed studies presented views or prototype screens, but clinical aims and use scenarios were mostly insufficiently described to ascertain their applicability to clinical contexts. This represented a challenge for the review process, which highlights the value of comprehensive and standardized descriptions for future evidence synthesis. Most studies focused on condition-specific biomarkers and health care use history to build the visualization to enhance specialists' clinical decisions, and the concept of team-based long-term care and features that could improve provider-provider or patient-provider communication were not common. Only 2 studies mentioned cross-organizational communication to improve the management of a chronic condition [START_REF] Panzarasa | A careflow management system for chronic patients[END_REF][START_REF] Richter | The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians[END_REF], which is essential to integrated care. The studies reported systems that were not evaluated in real-life settings to assess the impact of their implementation on care organization, quality, or effectiveness. Our search for subsequent articles that described further evaluation of the analyzed methods did not identify additional records.

From a technological perspective, although clinical information is readily available in current EHR systems, the diversity in patient data and the lack of interoperability between different health care organizations' HISs are still challenges to the secondary use of patient data [START_REF] Meystre | Clinical data reuse or secondary use: current status and potential future progress[END_REF][START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF]. As observed in this review, the main data source for most studies was EHRs. When other HISs were used, data acquisition processes and linkage methods were not always presented. Other system characteristics such as architecture and data security measures were available in some reports, such as in the work of Bettencourt-Silva et al [START_REF] Bettencourt-Silva | Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer[END_REF]; they reported a methodology to build data-driven pathways using patient-centric data from different databases and presented the data extraction process, the methodology, the building of an operational data store, the pathway and the analysis engine, and the visualization software. Finally, this was the only study in our sample to report and discuss quality indicators. The XSL • FO RenderX absence of crucial details concerning data acquisition, interdatabase linkage, data quality, and security not only posed a challenge in summarizing the methods outlined in this review but also hindered their reproducibility.

From a behavioral perspective, the technological innovations analyzed here did not clearly specify actions or use scenarios and did not always identify end users. This is common in health services research and explains in part the reason that suboptimal clinical practices persist and are associated with avoidable morbidity and mortality despite the potential benefits of uptake of innovations to care organization and practice [START_REF] Presseau | Action, actor, context, target, time (AACTT): a framework for specifying behaviour[END_REF]. It also posed a barrier to the review process, making it harder to fully understand the context in which these methods were meant to be applied. Furthermore, specifying behaviors represents a starting point for identifying their barriers and facilitators (at individual, team, and system levels) and strategies or techniques to promote behavior change in the agreed direction, for example, by features integrated into the tool or by additional user training. Using longitudinal health care visualizations and decision support systems, care coordination can be promoted at the HCP level through functions such as messaging systems to provide cues for action or feedback on behavior from encounter history [START_REF] Marchibroda | The impact of health information technology on collaborative chronic care management[END_REF]. The studies included did not mention strategies to promote intended behaviors or user training, which suggests that integration into clinical care was not envisaged yet. Moreover, in the studies included, patients were not systematically considered as possible users, even when aims included improving the shared decision-making process at the point of care. Considering that access to actionable information can lead to better chronic disease management [START_REF] Klein | Use of the blue button online tool for sharing health information: qualitative interviews with patients and providers[END_REF][START_REF] Mohsen | The blue button project: engaging patients in healthcare by a click of a button[END_REF], including patients as end users and integrating their preferences in the development phase of such tools have the potential to improve the shared decision-making process.

The differences among the included studies in terms of the type of information reported highlight the need for common reporting guidelines and the development and adoption of best practices in this research area. Following the adapted technology development framework we applied in this review [START_REF] Moreno-Conde | Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis[END_REF], we propose that new projects developing data-driven long-term CDP visualization and quantification methods could benefit from descriptions including clinical (aim, information used, relevance, and evaluation criteria); technological (which could be followed sequentially in the method development); and behavioral aspects (identifying end users and their actions expected following the established behavioral frameworks, such as the AACTT; Figure 3). Standardizing terminology for describing the different aspects is paramount for facilitating evidence synthesis and the evolution of this field. The development of these methods could follow a similar process with CIM development, including (1) defining scope and the work team comprising potential end users (HCPs, patients, and managers); (2) performing a domain analysis; (3) designing the tool; (4) defining implementable tool specifications; (5) detailing dataflow and transformation; (6) addressing data protection; [START_REF] Siqueira Do Prado | Quantification and visualisation methods of data-driven chronic care delivery pathways: protocol for a systematic review and content analysis[END_REF] performing validation by end users; and (8) disseminating results. Moreover, we consider that the development of data-driven long-term CDP frameworks could benefit from following established frameworks for developing complex health interventions [START_REF] Skivington | A new framework for developing and evaluating complex interventions: update of medical research council guidance[END_REF][START_REF] Fernandez | Intervention mapping: theory-and evidence-based health promotion program planning: perspective and examples[END_REF][START_REF] Van Gemert-Pijnen | A holistic framework to improve the uptake and impact of eHealth technologies[END_REF], which would improve the design and reporting of such system-level innovations.

Limitations

Our review presents several limitations that would need to be addressed in future work. First, although we used several keywords and terms in different databases, it is likely that our search might have missed relevant publications due to the diversity of terminologies used in describing CDPs. This highlights the importance of developing common terms and definitions for this type of work. Second, because health care technology is a rapidly evolving field, not all technologies developed are disseminated in scientific publications. Our review did not include studies that were not published as peer-reviewed papers because it focused on projects that aimed to produce and disseminate scientific evidence to support their tool. Therefore, our results should be interpreted as a synthesis of the best available evidence. Future work could review other types of literature to identify a broader range of methods developed in more diverse contexts. Third, we focused on CDP description methods applied to people living with chronic conditions, in view of the importance of care coordination in long-term care. Thus, we have excluded visualization methods applied in other settings [START_REF] Plaisant | LifeLines: using visualization to enhance navigation and analysis of patient records[END_REF][START_REF] Hirsch | HARVEST, a longitudinal patient record summarizer[END_REF][START_REF] Glicksberg | PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model[END_REF][START_REF] Happe | A visual approach of care pathways from the French nationwide SNDS database -from population to individual records: the ePEPS toolbox[END_REF], which may have the potential to be applied in long-term care. Future applications of these methods may consider the specific requirements identified in this review relevant to these use scenarios.

Conclusions

In conclusion, this review is the first to describe the emerging field of data-driven long-term CDP visualization and quantification methods, systematically examine published research, and propose ways to structure the conduct and reporting of such studies in the future. These methods share common elements with health information technology-supported clinical pathways, but they represent a distinct category of innovations given the use of retrospective EHD data and the data display that considers the sequence of events in a timeline (temporal dimension). Moreover, we believe that data-driven long-term CDP visualization tools can be used to enable integrated care, combining different data elements in comprehensive views to be used at the point of care. To address the issue of different terminologies used to describe CDPs, we propose data-driven care delivery pathway as a unifying term and welcome further clarifications and agreement on the terminology. This review has allowed for the description of a research area that is under development and subject to multiple challenges, which require concerted efforts for standardization.

Figure 2 .

 2 Figure 2. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart describing the identification and screening of records and selection of included studies.

hPICASO:

  Personalised Integrated Care Approach for Service Organisations and Care Models for Patients with Multi-Morbidity and Chronic Conditions.

Figure 3 .

 3 Figure 3. Key points to developing new data-driven care delivery pathway visualization and quantification methods.

Table 1 .

 1 Characteristics of selected studies.

	JOURNAL OF MEDICAL INTERNET RESEARCH			Siqueira do Prado et al
	Study, year	Title	Country	Type of study	Objectives	Studied population
							Patients, n	Conditions
	Warren et al [25] 1999	Chronic disease coor-dinated care plan-ning: flexible, task-centered decision support	Australia	Descriptive	•	Descriptive: CPOL a , a decision support system for chronic care planning at SA HealthPlus by inte-grating relevant information flows at the point-of-care user interface and architecture	4000	High-use pa-tients in South Australia in 10 groups includ-ing diabetes, cardiac, aged care, and lung
								disease
	Panzarasa et al [27] 2004	A careflow manage-ment system for chronic patients	Italy	Descriptive	•	Descriptive: infrastructure (CfMS b ) for enabling the cross-organization-al communication process of	Not reported	Type 1 and type 2 diabetes
						chronic disease management in dia-
						betes care
	Mabotuwana and Warren [26] 2010	ChronoMedIt-a computational quali-ty audit framework for better manage-ment of patients with chronic condi-tions	New Zealand	Descriptive and validation	• •	Descriptive: ChronoMedIt c is a framework that takes temporal considerations into account when formulating and executing audit criteria in chronic disease manage-ment Validation: to apply the framework	1286	Hypertension
						to 2 practices' data sets to detect
						patients with suboptimal manage-
						ment
	Husain et al	HERALD (Health	United King-	Descriptive	•	Descriptive: procedures linking pa-	715	Ankylosing
	[23] 2012	Economics using	dom			tient-derived questionnaire data		spondylitis
		Routine				with routinely collected information
		Anonymised Linked				and secondary care clinical data sets
		Data)				to conduct health economics analy-
						ses
					•	Validation: to map patients journeys
						in an ankylosing spondylitis cohort
						in 3 different settings (general
						practitioner, outpatients, and inpa-
						tients)
	Hsu et al [19]	Context-based elec-	United States	Descriptive	•	Descriptive: AdaptEHR, a context-	283	Glioblastoma
	2012	tronic health record: towards patient spe-cific healthcare				based EHR d using biomedical on-tologies and (graphical) disease models as sources of domain		multiforme (brain cancer)
						knowledge to identify relevant parts
						of the free-text record to extract,
						aggregate, map on ontologies and
						display in the patient record for
						different users depending on their
						information needs to inform medi-
						cal decision-making
					•	Validation: to implement the
						framework in a system called
						AdaptEHR to present and synthe-
						size information from neuro-oncol-
						ogy patients
	XSL • FO
	RenderX

J Med Internet Res 2023 | vol. 25 | e49996 | p. 5 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes) a CPOL: Care Planning On-Line. b CfMS: Careflow Management System. c ChronoMedIt: Chronological Medical audit. d EHR: electronic health record. e MHT: MyHealthTeam. f CKD: chronic kidney disease. g ET2: EventThread 2.

Table 2 .

 2 Technological characteristics of the care delivery pathway quantification and visualization methods selected (n=14).

	Categories (n=14)	War-	Pan-	Mabo-	Hu-	Hsu	Sun	Bet-	Zhang	Zhang	Zhang	Litch-	Guo	Umer	Richter	Studies
		ren	zarasa	tuwana,	sain	et	et	ten-	and	and	and	field	et al	et al	et al	report-
		et al	et al	and War-	et al	al	al	court-	Pad-	Pad-	Pad-	et al	[28]	[29]	[30]	ing, n
		[25]	[27]	ren [26]	[23]	[19]	[20]	Silva	man	man	man	[24]	2019	2019	2021	(%)
		1999	2004	2010	2012		2013	et al	[5]	[6]	[21]	2017
								[22]	2015	2016	2017
								2015

Scope definition leading to selection of the domain and selecting relevant experts

	Information on the	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	14 (100)
	domain to be cov-
	ered and whether the
	scope is local or
	wider are presented
	The study involved	✓		✓		✓		✓		✓	✓	✓	✓	✓	✓	10 (71)
	a group of experts
	based on the care
	setting, health care
	activities, and clini-
	cal requirements
	Expected uses or use		✓	✓							✓		✓	✓		5 (36)
	case scenarios are
	presented

Analysis of the information covered in the specific domain

	Categories (n=14)	War-	Pan-	Mabo-	Hu-	Hsu	Sun	Bet-	Zhang	Zhang	Zhang	Litch-	Guo	Umer	Richter	Studies
		ren	zarasa	tuwana,	sain	et	et	ten-	and	and	and	field	et al	et al	et al	report-
		et al	et al	and War-	et al	al	al	court-	Pad-	Pad-	Pad-	et al	[28]	[29]	[30]	ing, n
		[25]	[27]	ren [26]	[23]	[19]	[20]	Silva	man	man	man	[24]	2019	2019	2021	(%)
		1999	2004	2010	2012		2013	et al	[5]	[6]	[21]	2017
								[22]	2015	2016	2017
								2015
	If applicable, this or-														✓	1 (7)
	ganization oversees
	quality review, publi-
	cation, and relation-
	ships with other
	projects working on
	the same domain
	Dataflow and transformation
	Linkage between da-	✓	✓	✓		✓	✓	✓	✓		✓	✓			✓	7 (7)
	ta sets is described
	The architecture of	✓	✓	✓		✓	✓	✓	✓		✓	✓		✓	✓	8 (57)
	the tool is presented
	Data protection for the			✓	✓		✓	✓	✓	✓		✓	✓		✓	9 (64)
	development is described
	Subcategories with infor-mation reported, n (%)	6 (43)	7 (50)	10 (71)	3 (21)	7 (50)	6 (43)	11 (79)	3 (21)	4 (29)	6 (43)	5 (36)	10 (71)	10 (71)	11 (79)	N/A a
	Clinical scenarios,		✓			✓		✓						✓		4 (29)
	workflows, and
	users are understood
	to determine the data
	items to be used in
	the method
	Existing systems are		✓					✓					✓	✓		4 (29)
	described (how they
	have been imple-
	mented and docu-
	mented)
	Design of the tool: the set	✓	✓	✓		✓	✓	✓			✓	✓	✓	✓	✓	11 (79)
	of attributes associated
	with the method is de-
	tailed
	Implementable technical		✓	✓		✓		✓			✓		✓	✓	✓	8 (57)
	specification is described
	Validation
	The study presents	✓		✓		✓	✓	✓	✓	✓	✓		✓	✓	✓	11 (79)
	prototype screens
	The method is vali-			✓			✓	✓				✓	✓	✓	✓	7 (50)
	dated (eg,: training
	or testing data, pilot
	study, implementa-
	tion test, etc)
	Governance
	There is an organiza-														✓	1 (7)
	tion responsible for
	developing and
	maintaining the
	method

a N/A: not applicable.

Table 3 .

 3 Behavioral elements present in the care delivery pathway quantification and visualization methods selected. The text describing the scenarios was extracted from the original articles.

	Actor	Action	Context	Time	Target

Panzarasa et al [27] 2004 Scenario 1

J Med Internet Res 2023 | vol. 25 | e49996 | p. 3 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 4 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 6 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 7 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 8 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 9 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 10 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 12 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 13 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 15 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 16 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

J Med Internet Res 2023 | vol. 25 | e49996 | p. 17 https://www.jmir.org/2023/1/e49996 (page number not for citation purposes)

Acknowledgments

This research was supported by an Initiative D'EXcellence Lyon-Saint-Etienne (IDEXLYON) (16-IDEX-0005) fellowship grant (2018-2021). ALD was affiliated with the INSERM Unit U1290 -Research on Healthcare Performance at the time of the review and is currently affiliated with the Health Technology Assessment in Primary Care and Mental Health (PRISMA), Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.

Authors' Contributions

LSdP contributed to conceptualization, methodology, investigation, formal analysis, data curation, and writing (original draft); SA contributed to conceptualization, methodology, investigation, writing (review), and editing; MV contributed to methodology, investigation, writing (review), and editing; AMS was responsible for methodology, supervision, writing (review), and editing; DD was responsible for methodology, funding acquisition, supervision, writing (review), and editing; ALD contributed to conceptualization, methodology, investigation, formal analysis, funding acquisition, supervision, writing review, and editing.

Conflicts of Interest

None declared. ©Luiza Siqueira do Prado, Samuel Allemann, Marie Viprey, Anne-Marie Schott, Dan Dediu, Alexandra Lelia Dima. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.12.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Multimedia Appendix 1