Systematic Review: Time duration as a predictor of quality of healthcare
Creators: name: León-García, Montserrat
name: Wieringa, Thomas
name: Hidalgo, Jessica
name: Espinoza, Nataly
name: Leal, María
name: Montori, Victor
name: LeBlanc, Annie
name: Ospina, Naykky
name: Brito, Juan
name: Rocha, Claudio
name: Zeballos-Palacios, Claudia
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Medicine and Health Sciences
subject: Health and Medical Administration

description: BACKGROUND During the last 30 years, quality of care has been a major goal that public health aim to accomplish. In essence, policy-makers strive to improve their health systems by incorporating interventions where users get the care they need, and that the care they get is effective.(1) The challenge to define quality of care has hampered the development of specific policies or interventions and the research work conducted around frameworks to define it represent the most important contributions in the quality field. In 1980, Donabedian, one of the main contributors in the field defined care of high quality as “that kind of care which is expected to maximize an inclusive measure of patient welfare, after one has taken account of the balance of expected gains and losses that attend the process of care in all its parts”(2) Acknowledging the differential nuances between the different definitions; all of them seem to contain two components that are important to people: the first component is providing care of high technical quality understanding technical care as the application of clinical medicine to a personal health problem where care should be appropriate and necessary. The second component is that all patients wish to be treated in a humane and culturally appropriate manner and be involved in their health decision-making.(3) Campbell et al. created a framework(1) to define quality of care where they identify three dimensions: the structure of health care, actual care given (process) and the consequences of the interaction between individuals and a health care system (outcome). For the purpose of this review we will be focusing in the process dimension. Process is the actual delivery and receipt of care. Processes of care involve interactions between users and the health care structure, i.e. what is done to or with users, hence the clinical encounter and what happens in these visits, plays an important role. Clinical visit is a pivotal part of healthcare, as is it this space that patients and clinicians collaborate to understand the clinical situation of the patient and the best step moving forwards. Clinical evidence suggest that the duration of clinical visits have remained stable during the last few decades, despite significant changes in the way healthcare is delivered (e.g. availability of electronic medical records). Although, both patients and clinicians, identify time for consultation as a barrier to high quality care, the extent to which the duration of clinical visits affects quality of care is unknown. Wilson and Childs work is focused on effectiveness of interventions aimed at changing the length of primary care physicians' consultations.(4–6) The Cochrane systematic review they published in 2016 included outcomes related with effectiveness and efficiency and concluded that they were unable to determine whether altering the length of consultation leads to changes in provider's performance nor the effect it has on referrals, examinations and prescriptions. The findings were based on very low‐certainty evidence. The review lacks of the inclusion of outcomes more related to other aspects of quality of care regarding the interaction in the encounter; such as patient-centeredness, equity, safety, legitimacy…, and limits this dimension to the outcome patient’s satisfaction- which they were also uncertain whether altering the length of consultation increases patient satisfaction. Other authors that also aim to evaluate time as a predictor of quality of care focused too on patient satisfaction.(7,8) There is a need for a contemporary assessment of clinical visit time as a predictor of quality care, and encompassing a broader definition of quality of care) that englobes a further understanding if the care given was needed and appropriate. The results of this systematic review will be of interest for patients, clinicians, policy makers and other stakeholders, as a better understanding of the relationship between clinical visit duration and quality of care can inform practice design that is supportive of care, by providing the time for patients and clinicians to work together. METHODS We will conduct a systematic review and will adhere to the applicable items of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. We registered this protocol in the open science framework registry:osf.io/svcwq. Any amendment to the protocol and the supporting rationale will be reported. Eligibility criteria We will include studies reporting on the duration of the outpatient visit of adult patients and its association to quality of care: 1. Population of interest: Adults visiting an outpatient clinic (in person or telemedicine). We wil exclude: encounters in the hospital, emergency department or urgent care center; children/pediatrics; and, simulated patients or visits. The kind of clinician is not an eligibility criterion: outpatient visits with a practice nurse or doctor, a pharmacist, or a therapist of any sort are eligible. 2. Independent variable: Measure of duration of the consultation. Encounter length must be used as an explanation or predictor of the quality care (meaning that the study is able to make inferences about the relationship between length of the consultation and quality of care (e.g., do shorter/ longer encounters impact on the quality of health care?). We would exclude articles in which encounter duration is an outcome of a practice change or intervention. Likewise, we would exclude articles in which the time measured is or includes time spent waiting for an appointment, traveling to an appointment, or waiting in an appointment. 3. Dependent variable: Measures of quality of care (examples of signaling questions): • Satisfaction: “did the care you received meet or exceed your expectations?”; but not just satisfaction with the duration of the visit or preference for longer/shorter visits. • Efficiency or value (impact on care per unit of time or money) • Effectiveness or efficacy (impact on health outcomes) • Patient-centeredness: “would a short visit impact on communication, preferences reported, involvement” • Safety: “would a short visit impact on how safe a treatment/intervention is” • Equity: “would a short visit differentially affect access, care, or outcomes by race, ethnicity, gender, or other marker of socioeconomic status” • Timeliness Information sources and search strategy We will design and execute a comprehensive search in multiple databases. We will search each database from their inception to June 23rd, 2021. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process &amp; Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus. The search strategy was designed and conducted by an experienced librarian (add initials), with input from the study's principal investigator (add initials). Controlled vocabulary supplemented with keywords will be used to search for studies of time factors in the clinical encounter and their effect on quality of care. The actual strategy listing all search terms used and how they are combined is available in the appendix. The leading search concepts will be:  Outpatient visits (consultation)  Time (duration, length, minutes); Quality of care (equity, patient-centeredness, effectiveness, efficiency, timeliness, safety, value, satisfaction). Selection of studies We will include quantitative primary studies (both experimental and observational studies) and primary qualitative studies, as well as those following a mixed-method or multiple methodology. Protocols, systematic reviews, editorials, commentaries, position papers, guideline documents, or narrative reviews will be excluded. We would review reference lists of included studies. We will manage references with Rayyan. Pairs of authors will conduct the screening of title and abstract references and selection of studies. Full-text appraisal would be also conducted in pairs and reasons for exclusion will be collected. Disagreements will be solved by discussion or, in the case discussion will not lead to consensus, with the help of a third author. Agreement will be measured using the kappa or phi statistics, as appropriate (the latter is appropriate when the distribution of agreement is extreme). Data extraction We will extract data using Google forms which uses an Excel format. We will design and pilot-test one form for each type of study (quantitative, qualitative or mixed methods). After initial calibration of 10% of the included studies between pairs of authors, one author will extract data from the included studies, and a second author will check the data against the full text for accuracy. We will solve disagreements by consensus or, if needed, with the help of a third author. We will collect the following information: • study characteristics (author, publication title, country and year of publication) • Justification for studying the association • Population/ Setting:  Type of patients: new or established; one or multiple diseases and type.  Type of clinicians: clinician gender; individual or group; specialist vs. generalist; trainee vs. faculty; type of practices; health care background  Setting: Primary or specialty care; healthcare was funded privately or publicly and took place in private/publicly funded institutions • Outcomes  Length of time spent in the visit: measures of time, patient reported measure, descriptive theme.  Measures and degree of quality (equity, patient-centeredness, effectiveness, efficiency, timeliness, safety, satisfaction, preferences, values) and instrument to collect the outcome; or descriptive theme. Data synthesis and reporting Quantitative evidence: To analyse and synthesize quantitative findings we will use narrative synthesis and will conduct metanalysis if applicable. We will perform descriptive statistics to characterize the length of consultation and quality of care measures (mean, SD for variables with a normal distribution and the median, interquartile range, for other distributions). For reporting, we will present results in summarized tables (grouping by relevant categories of outcomes). Comparative statistical tests will be used to look for differences in e.g. mean values, 95% confidence intervals and I2 statistic for heterogeneity (if pooled), or the range of mean values or other measures (if not pooled) across studies, the number of studies and the number of respondents for each outcome. We will perform statistical analyses using SPSS (IBM SPSS Statistics for Mac, Version 29.0. Armonk, NY: IBM Corp). Qualitative evidence. We will analyze and synthesize qualitative data using a thematic synthesis approach using NVivo 12. We will not specify any a priori theme and will generate free codes inductively. Quality assessment Two authors will independently assess the risk of bias of ultimately included studies to identify areas of strength and weakness of the existing evidence. Disagreements will be resolved through discussion. For quantitative studies: For randomized clinical trials we will be using RoB 2 tool (9)which provides a framework for considering the risk of bias in the findings of any type of randomized trial. The tool is structured into five domains which are: (1) bias arising from the randomization process; (2) bias due to deviations from intended interventions; (3) bias due to missing outcome data; (4) bias in measurement of the outcome; (5) bias in selection of the reported result. We will use ROBINS-I tool(10) for assessing risk of bias in non-randomized studies of interventions. This tool includes seven domains: The first two domains, covering confounding and selection of participants into the study, address issues before the start of the interventions that are to be compared (“baseline”). The third domain addresses classification of the interventions themselves. The other four domains address issues after the start of interventions: biases due to deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported result. For qualitative research studies, we used the CASP Qualitative Checklist tool(11) to appraise the methodological quality of individual studies and will assess the certainty of the evidence according to GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative Research) domains (methodological limitations, relevance, coherence, and adequacy). (12) For assessment of the full body of evidence, i.e., for each intervention and outcome, we will utilize the Grading of Recommendations Assessment (GRADE) approach. GRADE is a step-wise method to assess the certainty of evidence in the synthesis of scientific literature, such as reviews and guidelines, and is commonly used for the appraisal of effects found of health interventions in health care and the public health domain. The method is used to rate evidence per outcome and starts with a high rating for RCTs and low rating for all other study designs. Eight criteria, such as risk of bias or large magnitude of effect, are then used to either upgrade or downgrade the final rating to high, moderate, low, or very low quality of evidence. This way, the method can indicate how certain we can be of an effect and whether reliable recommendations for practice in health care can be made based on the evidence that is available.