Chronic disease has a wide impact around the world. The World Health Organization (WHO) reported that 63% of all deaths are from chronic disease (WHO 2011).
Missed hospital appointments is a commonly reported problem in healthcare services around the world; for example, they cost the National Health Service (NHS) in the UK millions of pounds every year (HES 2010). This unnecessary cost and change in public expectations has brought into question the efficiencies of secondary care appointment scheduling systems, particularly in chronic conditions. Alternative appointment systems have been explored to an extent. For example, the Expert Patient Programme (in the UK) was specifically aimed at people with long-term conditions (DoH 2001). While alternative forms of appointment scheduling may not be appropriate for all healthcare areas, those areas managing people who have long-term or chronic conditions may see some benefits.
In 2002, the WHO published a report highlighting the need for a model of care that more readily meets the needs of people with chronic conditions (WHO 2002). The authors suggested that innovations that build on evidence-based decision-making, have a population and quality focus, and are flexible to the needs and demands of the patient population should do well in improving the management of chronic conditions.
Description of the condition
Chronic conditions, defined as "diseases of long duration and generally slow progression" (WHO 2013) include diseases such as rheumatoid arthritis, asthma, cancer and diabetes, as well as others. People are faced with an opportunity to manage their condition but not cure it. Traditionally, people with these types of conditions are managed by the clinician through regularly scheduled appointments (e.g. one to four times per year) at outpatient clinics (Kirwan 1991; Probert 1993). These appointments often occur at a time when a person is feeling relatively well with little action taken as a result. Conversely, when symptoms recur or suddenly worsen it may be difficult to obtain immediate urgent appointments if needed. Subsequently, people are often unable to get help during periods of exacerbated disease due to the number of regular follow-up appointments also needed in the system. In some cases, conditions are managed in primary care; a number of studies reported on the success of similar systems in this setting (Liu 2010; Robinson 2010; Rose 2011).
Description of the intervention
A patient-initiated appointment system enables patients to make urgent appointments when they are going through a time when they feel they cannot manage their condition or where something has unexpectedly changed. The system does not completely replace the need for a scheduled follow-up appointment but the new system could decrease the number of follow-up appointments (e.g. every one to two years).
How the intervention might work
The patient-initiated appointment system could free up clinician time, therefore making the service more flexible for urgent appointments, while still being able to deliver a standard of care acceptable to patients. Using this type of service may also mean that the numbers of missed appointments are reduced (and therefore financial and resource costs too) as most patients will be attending because they need to or choose to, or both, and not just because the appointment is a requirement. There is a potential risk in situations where the patient fails to request an appointment at the time of relapse or escalation of their condition, and symptoms become worse, possibly critical. This risk is more likely when the appointment systems do not include a 'safety net' appointment system (an appointment which is scheduled by the clinician for a certain point in time to ensure the patient is using the system correctly) or when clinicians are unable to select appropriate patients for the patient-initiated appointment system pathway. In addition, there are elements of preventive health care or patient education that occur during a routine appointment that are not addressed during a patient-initiated appointment. This risk can be minimised by incorporating an appropriate checklist into a 'safety net' appointment. There have been several studies that have explored the effectiveness of patient-initiated appointment systems in primary care (Liu 2010; Robinson 2010; Rose 2011). The results of some of these studies suggest that patient initiation of care results in improvements in satisfaction with a reduced cost for care delivery.
Why it is important to do this review
There are a number of Cochrane reviews that have considered alternative methods to improve attendance to appointments (Car 2012; Reda 2012); however, none of these reviews has looked at the impact of patient-initiated appointment systems in secondary care. With the increasing focus on healthcare efficiencies and the increasing emphasis on enabling people to manage their own conditions (Nuffield 2011; WHO 2002), determining the benefits and harms of patient-initiated appointment systems in secondary care is crucial to understanding their worth for both healthcare systems and patients.
To assess the effects of patient-initiated appointment systems compared with usual care in people with chronic or recurrent conditions managed in the secondary care setting. In particular, we are interested in whether these appointment systems can effectively manage disease without causing harm to patients and whether costs related to the provision of the service can be reduced compared with usual care.
Criteria for considering studies for this review
Types of studies
This review will only include randomised controlled trials (RCTs), including cluster-randomised controlled trials (cluster-RCTs) (published and unpublished and in any language) that compare the use of patient-initiated appointment systems versus consultant-led appointment systems. We will not include a broader range of study designs, as RCT designs are able to inform on causal relationships more reliably than other study designs due to the level of bias intrinsic in their design; this may be particularly important with this topic with the progressive and fluctuating nature of long-term conditions. Furthermore, we believe that there is sufficient RCT evidence on this topic to make evidence-based recommendations.
Types of participants
Adults (18 years of age or older) diagnosed with a chronic or recurrent condition, such as rheumatoid arthritis, which is managed in secondary care.
Types of interventions
A patient-initiated appointment system, established in the secondary care system, where appointments can be initiated by the patient whenever they require support from a relevant health professional to manage their ongoing condition. The appointments will not be used for the purposes of diagnosis. Patient-initiated appointment systems may include a 'safety net' appointment where patients receive an annual or regularly scheduled appointment with a consultant. The comparator will be a consultant-led appointment system (usual care) where patients are given a scheduled appointment to see the relevant health professional (usually a consultant) in secondary care whether or not they require support. Other appointments may only be made in case of an emergency or a crisis.
Types of outcome measures
We will include studies that report one or more of the following outcomes. The primary outcomes are those that will be included in a 'Summary of findings' table, as well as the patient and clinician satisfaction outcomes, as these are all important for decision-making. We will construct the 'Summary of findings' table using Cochrane Effective Practice and Organisation of Care (EPOC) templates and worksheets for preparing a 'Summary of findings' table using GRADE (EPOC 2013).
- Participant outcomes, such as physical measures of health status or disease control (including harms), and quality of life.
- Service utilisation, such as frequency of visits/contacts with secondary care, missed appointments and waiting times.
- Resource use as financial and temporal costs to the healthcare system and the participant.
- Adverse effects.
- Other service utilisation such as frequencies of visits to other health-related clinicians as reported (including outside of secondary care).
- Clinician satisfaction.
- Participant satisfaction.
- Failures of the 'system' (e.g. how long participants are on the patient-initiated appointment system pathway but not using it correctly before the clinical team needs to re-instigate regular follow-up clinics).
Search methods for identification of studies
We will search the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO (via Ovid SP), Health Technology Assessment (HTA), NHS Economic Evaluation Database (NHS EED), DARE (via The Cochrane Library), CINAHL (via EBSCOhost), Health Management Information Consortium (HMIC) (via NHS Evidence), Proquest Dissertations and Science Citation Index (via Web of Science). We will search the databases from inception to the present date. We will use the search strategy in Appendix 1 for MEDLINE and adjust it for the other databases.
Searching other resources
We will also contact the authors of identified studies and carry out backwards and forwards citation searching of included articles. We will identify current research through searching the PROSPERO, Current Controlled Trials Register and the Medical Research Council (MRC) Clinical Trials Register.
Data collection and analysis
Selection of studies
Two review authors will independently screen all references at the title and abstract stage using prespecified inclusion criteria. A third review author will arbitrate any disagreements. References that apparently meet the inclusion criteria and references that do not provide enough information to make an informed and certain decision will go through to the full-text stage. At the full-text stage, two review authors will independently screen all references and with a third review author arbitrating any disagreements. We will record reasons for exclusion at the full-text stage.
Data extraction and management
Two review authors will independently complete data extraction. We will use a data extraction form to cover the relevant details, such as information on the participants, setting, interventions and comparisons, outcomes and study design, which will be piloted before use (Appendix 2). A third review author will resolve any discrepancies where necessary. We will enter data into Review Manager (RevMan 2012).
Assessment of risk of bias in included studies
Two review authors will independently assess the risk of bias using the Cochrane EPOC 'Risk of bias' tool (EPOC 2013). Items in this tool include sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective outcome reporting and other bias. A third review author will resolve any discrepancies where necessary. While we will use the 'Risk of bias' tool to inform the analysis and interpretation of the results, we will not use it as further criteria for excluding studies in the review. Instead, we will present the risk of bias across all studies in a table that will indicate if the study has a low/high/unclear risk of bias; this will allow the reader to make their own judgements about the quality of the evidence available in this area.
Measures of treatment effect
We expect to find a wide range of data (measures of treatment effect). We will quantify continuous outcomes using the mean difference with 95% confidence intervals (CIs) and where studies have used different continuous measures to quantify a given outcome (e.g. quality of life, carer satisfaction), we will pool the standardised mean difference (with 95% CIs). We will use risk ratios with 95% CIs for any dichotomous data. Should data require any further processing/analysis, we will use the expertise of our statistician.
Unit of analysis issues
Where studies have several points of follow-up, we will select the longest point of follow-up for each outcome and will assess the impact of point of follow-up in the sensitivity analysis. Where studies used a cluster-RCT design but do not allow for clustering in the analysis, we will adjust the standard error of estimate appropriately based on the mean cluster size (i.e. number of participants per cluster) and assumed values of the intracluster correlation coefficient (ICC) within a plausible range (e.g. if the cluster is the hospital then we will assume values for the ICC between 0 and 0.3 for patient outcomes).
Dealing with missing data
Where the authors have not provided a point estimate or enough information, or both, to calculate the standard error of the estimate, we will contact them directly to request this information.
Assessment of heterogeneity
We will quantify heterogeneity across estimates using the I
Assessment of reporting biases
We will examine the likelihood of publication bias using Funnel plots and Egger's regression test for asymmetry (using the metabias command in Stata software) (StataCorp 2001).
Our analyses will combine estimates of the effect of patient-initiated appointment systems on the study outcomes. We will use random-effects meta-analysis to pool the estimates using the DerSimonian-Laird method. We will perform the meta-analysis using the estimates of association and standard errors using RevMan 2012. We will quantify the effect on continuous outcomes using the mean difference and on any binary outcomes using the risk ratio. Where studies have used different continuous measures to quantify a given outcome (e.g. quality of life, carer satisfaction), we will pool the standardised mean difference. If a meta-analysis is not possible, then we will use the median of medians approach to summarise the intervention effects across the studies. We will present the interquartile range of the study-specific medians. We will apply this approach on the effect size scale (i.e. mean difference between trial arms divided by the standard deviation in the control arm) for continuous outcomes. For binary outcomes, we will apply the approach on the log risk ratio (or log odds ratio) scale for binary outcomes before back-transforming the median onto the risk ratio (or odds ratio) scale.
Subgroup analysis and investigation of heterogeneity
If there is heterogeneity and a sufficient number of studies we will use meta-regression to investigate whether the pooled estimates differ across subgroups defined by potential effect modifiers, specifically health condition (if more than one study is identified in a specific condition, e.g. rheumatoid arthritis, Parkinson's disease, and so on) and age. We are interested in health condition because the nature of the condition may influence how the intervention is received and experienced.
We will conduct sensitivity analysis in the case of missing data, particularly with regard to where length of follow-up, attrition rates and units of analysis are missing or unclear. Where possible we will also base sensitivity analysis on aspects of risk of bias.
We would like to acknowledge the helpful feedback and comments from the peer reviewers Craig Ramsay, Leora Horwitz, Megan Prictor and Sasha Shepperd, and the help and advice from Michelle Fiander and Denise O'Connor.
Appendix 1. Search Strategy (MEDLINE)
Database: Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE(R) <1946 to Present>
1 (patient* adj3 initiate*).ti,ab.
2 (patient* adj3 led).ti,ab.
3 (patient* adj3 access*).ti,ab.
4 (patient* adj3 request*).ti,ab.
5 self refer*.ti,ab.
6 open access*.ti,ab.
7 direct access*.ti,ab.
8 advanced access*.ti,ab.
10 same day schedule*.ti,ab.
11 shared care.ti,ab.
12 (patient* adj (choice or choosing)).ti,ab.
13 "out of hours".ti,ab.
14 Health Services Accessibility/og [Organization & Administration]
16 exp "Appointments and Schedules"/
18 (outpatient* or out patient*).ti,ab.
19 (clinic or clinics).ti,ab.
20 Outpatient Clinics, Hospital/
21 ((follow up* or followup*) and (visit* or consultation*)).ti,ab.
22 (check up* or checkup*).ti,ab.
23 secondary care.ti,ab.
25 15 and 24
26 (patient* schedule* adj (appointment* or visit* or follow up)).ti,ab.
27 (same day adj (appointment* or visit* or schedule*)).ti,ab.
28 (open access adj (appointment* or visit* or schedule*)).ti,ab.
29 (patient adj3 access* adj3 (follow up* or visit* or appointment*)).ti,ab.
30 (demand based adj (scheduling or appointment* or visit* or follow up)).ti,ab.
31 advanced access scheduling.ti,ab.
32 (patient initiated adj3 (follow up* or appointment*)).ti,ab.
34 25 or 33
Appendix 2. Data extraction form
Please note that the first column in this table should be wider but something in the formatting went wrong here.
Contributions of authors
Rebecca Whear was involved in conceiving, designing and co-ordinating the protocol. She will be involved in the ongoing nature of the review including screening search results and retrieved papers against eligibility criteria, appraising the quality of papers and data extraction. She will also be involved in the analysis and interpretation of the data, data management and in writing up the final review.
Ken Stein was involved in conceiving, designing and co-ordinating the protocol. He will be involved in making final screening decisions, appraising the quality of papers and will be involved in the interpretation of the data in the final review. Ken will be involved in providing a methodological and policy perspective within writing up and giving advice on the review. He has also been key in securing funding for the review.
Joanna Thompson Coon was involved in conceiving, designing and co-ordinating the protocol. She has also been involved in informing the search strategies, and will be involved in screening search results and retrieved papers against eligibility criteria, appraising the quality of papers, extracting data from papers, analysis and interpretation of data, and providing comments on the final review.
Mark Perry was involved in conceiving and designing the protocol. He was also involved in determining the search strategies and will be involved in interpreting the data from a clinical and policy perspective. He will also be involved in writing the final review.
Obi Ukoumunne was involved in designing the statistical methods of the protocol. He will mainly be involved in the analysis and interpretation of the final review from a methodological perspective and will be involved in writing and editing the final review.
Morwenna Rogers was involved in designing the protocol. She led the design of the search strategies and will lead the literature search and help with document retrieval. She will also be involved in writing and editing parts of the final review.
Rebecca Abbott was involved in editing the protocol. She will also be involved in screening search results and retrieved papers against eligibility criteria, appraising the quality of papers and extracting data from them in the final review. She will also be involved in data management and writing for the final review.
Declarations of interest
Dr Mark Perry is currently involved with implementing a patient-initiated clinic for people with rheumatoid arthritis at Plymouth Healthcare NHS Trust.
Joanna Thompson-Coon, Rebecca Whear, Ken Stein, Morwenna Rogers and Mark Perry have been involved in a related review that has not yet been published.
Rebecca Abbott has no competing interests.
Sources of support
- No sources of support supplied
- National Institute of Health Research, UK.This systematic review will be funded by the National Institute for Health Research (NIHR) through Peninsula CLAHRC. This review will present independent research commissioned by the NIHR. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.