Original research•

Codesigned standardised referral form: simplifying the complexity

Abstract

Background Referring providers are often critiqued for writing poor-quality referrals. This study characterised clinical referral guidelines and forms to understand which data consultant providers require. These data were then used to codesign an evidence-based, high-quality referral form.

Methods This study used both observational and quality improvement approaches. Canadian referral guidelines were reviewed and summarised. Referral data fields from 150 randomly selected Ontario referral forms were categorised and counted. The referral guideline summary and referral data were then used by referring providers, consultant providers and administrators to codesign a referral form.

Results Referral guidelines recommended 42 types of referral data be included in referrals. Referral data were categorised as patient demographics, provider demographics, reason for referral, clinical information and administrative information. The percentage of referral guidelines recommending inclusion of each type of referral data varied from 8% to 77%. Ontario referral forms requested 264 different types of referral data. Digital referral forms requested more referral data types than paper-based referral forms (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01). A codesigned referral form was created across two sessions with 29 and 21 participants in each.

Discussion Referral guidelines lack consistency and specificity, which makes writing high-quality referrals challenging. Digital referral forms tend to request more referral data than paper-based referrals, which creates administrative burdens for referring and consultant providers. We created the first codesigned referral form with referring providers, consultant providers and administrators. We recommend clinical adoption of this form to improve referral quality and minimise administrative burdens.

What is already known on this topic

  • Healthcare providers are burning out due to increasing administrative burdens, and referral processes contribute to these administrative burdens.

What this study adds

  • Canadian referral guidelines are ambiguous and not specific enough to facilitate improvements to referral content quality.

  • Digital referral forms are significantly longer than paper referral forms, which contributes to administrative burdens.

  • The codesigned referral form template clearly defines which referral data elements to include in referral forms while reducing requests for extraneous information.

How this study might affect research, practice or policy

  • This study may help inform future referral forms, digital referral system development and data structures, and health policies to minimise the administrative burdens faced by healthcare providers, while improving the quality of referrals.

Introduction

Referral letters to consultant providers have been criticised for their poor quality due to the omission of relevant and important referral data dating back to the early 1990s.1 More recent literature has identified that referral letters lack important information, such as patient contact information, reason for referral, presumptive diagnosis, symptoms and physical exam findings.2 3 Poor quality and incomplete referrals can delay patient care, leading to patient harm and decreased quality of care.2 4 The cause of care delays is in part due to administrative burdens, as consultant providers must request missing information from referring providers,5 who likely did not realise that essential referral data were missing in their initial referral. Attempts have been made to mitigate this issue by defining essential referral data through surveys,1 creation of referral quality scoring systems6 7 and referral guidelines.8 However, studies continue to critique the quality of referrals.4 To our knowledge, no published literature has characterised the referral data that is requested by consultant providers based on clinically used referral forms at a system level. Nor has any study codesigned a referral form with referring providers, consultant providers and administrators.

Creating consensus on which referral data are required by consultants is important for improving referral quality and the transition to digital referral systems, like eReferral.9 Development of digital referral systems requires clearly defined data fields10 over traditional free-text letters. The main benefits of eReferral are that referring providers can send referrals via the internet instead of fax, find consultant providers closer to the patient or who have shorter wait times and patients receive email notifications about their referrals as they are triaged and booked.9 The timing of this study is important since eReferrals are becoming more common in Canada,11 meaning there is an opportunity to create standardised referral forms prior to widespread clinical adoption. To do this, we followed the Canadian Medical Associations’ 2014 recommendation to codesign referral forms.8

This codesign initiative was also in response to the increasing administrative burdens on primary care providers. In 2023, primary care providers in Ontario, Canada were spending 19.1 hours per week on administrative tasks.12 These administrative burdens arise from: detailed clinical documentation and data entry; inefficient user interfaces; cognitive burdens caused by reminders and irrelevant or redundant patient data and management of clinical messages and inboxes.13 This is consistent with other findings that healthcare providers are spending at least 2 hours on administrative tasks for each hour of direct patient contact.14 Importantly, primary care is experiencing the highest level of administrative burdens, leading to provider burnout.13 In Canada, 53% of primary care providers report burnout, 61% report experiencing significant emotional distress, 64% report their jobs are highly stressful, 76% report a significant increase in workload since 2020 and many plan to stop providing patient care in the next 1 to 3 years.15 Accordingly, it is essential that initiatives like this are undertaken to reduce administrative burdens and improve provider experiences to avoid future health human resource crises.

This study aimed to establish consensus on which referral data are essential for high-quality referrals. This was accomplished by characterising Canadian referral guidelines and the referral data fields on publicly available and clinically used referral forms from Ontario, Canada. Referring providers, consultant providers and clinic administrators then codesigned a standardised referral form based on these findings. This codesigned referral form was then clinically used on the eReferral platform. Subsequently, both referring and consultant providers were surveyed to report their clinical experience using this codesigned referral form. The primary outcome of this study was the creation of an evidence-based codesigned referral form.

Methods

Review of referral guidelines and policy statements

A review of the current Canadian referral guidelines was completed. Guidelines and policy statements were collected from national, provincial and territorial medical licensing bodies or medical associations (online supplemental table S1). Each document was reviewed, and all referral data recommended to include in referrals were recorded and categorised by data type in tabular format.

Characterisation of referral data fields on clinically used referral forms

Clinically used and publicly available paper referral forms from Ontario, Canada were collected from OSCAR EMR16 and South West Primary Care Alliance (SWPCA).17 These websites are the most comprehensive repositories of paper referral form stored as PDFs (Portable Document Format) and images in Ontario. All referral forms on the websites were extracted using Web Scraper18 into a Microsoft Excel19 spreadsheet containing the clinic or consultant provider name, geographic region, specialty, type of referral and URL to each form. All digital referral forms on eReferral9 and the corresponding clinic or consultant provider name, geographic region, specialty, type of referral and URL were provided by the eHealth Centre of Excellence20 in a Microsoft Excel19 spreadsheet. These two files were combined, then all forms were manually reviewed to exclude administrative tools, clinical tools, laboratory requisitions, government programme application forms, diagnostic imaging forms and duplicates since the focus of this study was consultation request forms.

One-hundred and fifty referral forms were randomly selected from the included forms. Each form was assigned a random number, sorted by number and the top 150 using Microsoft Excel.19 Each form was manually reviewed by author SL, who is a practising family physician. Each referral data field and corresponding format (informational, free text, check boxes, attachments) were recorded using Microsoft Excel.19 All fields were highlighted after recording to ensure complete data capture. Referral data were later categorised based on the specific data requested. Clinical judgement was required to differentiate between referral data types.

Statistical analysis

All data recording and statistical analysis were performed using Microsoft Excel.19 The total number of unique referral data fields on each form was determined and the mean number of data fields for paper and digital referral forms was calculated. The average number of informational, checkboxes, free text entry and attachment requests were then calculated for paper and digital referrals. Unpaired t-tests assuming unequal variances were then performed to determine whether there are differences in the average number of unique referral data fields between paper and digital referral forms. A subanalysis was also performed with unpaired t-tests assuming unequal variances for each format of requested referral data.

Codesigned referral form

Two codesign sessions were completed where referring providers, consultant providers and healthcare administrators collaborated to create a standardised and generic referral form. Participants were recruited by email through local hospital and primary care organisations. Participating referring providers included primary care physicians and nurse practitioners. Participating consultant providers included specialist physicians and nurse practitioners. Participating administrative providers included secretarial staff and clinic managers. Participants were provided with prereadings detailing the review of referral guidelines and review of referral data, outlined above. Participants discussed each category of referral data in a facilitated open forum and identified which information they felt should always be included in all referrals. Participants’ decisions and comments were recorded during the session, then used to create a standardised referral form.

In the second session, participants discussed each section of the referral form and commented on their impression of the created form. Participants were asked to identify any referral data that were missing or that required revision. Participants’ decisions and comments were recorded during the session and then appropriate revisions were made to the standardised referral form.

Codesigned referral form user experience

The codesigned referral form was then used in Ocean eReferral for a period of 5 months prior to seeking feedback. Referring providers that sent referrals using the codesigned referral form were contacted to provide feedback on their experience. These referring providers were contacted via email and provided a URL to a survey about their experience using the codesigned referral form. Specifically, the question How was your experience completing Ocean eFax referral forms compared to normal fax-based referrals? was used to assess providers’ experience using the codesigned referral form. Respondents rated their experience using a Likert scale indicating, Excellent, Good, Fair, Poor, Very Poor, or Not applicable.

Ethics approval

This study did not require ethics approval as it used publicly available information and is quality improvement in nature as per the University of Ottawa Research Ethics Board.

Results

Review of referral guidelines and policy statements

Review of the national and provincial referral guidelines identified 7 categories of referral data and 42 specific types of referral data for inclusion in referral letters (table 1). The number of guidelines recommending each type of referral data was variable from 1 (8%) to a maximum of 10 (77%). There were no referral data types that were recommended to include in referrals by all guidelines. Referral guidelines were noted to provided ambiguous referral data inclusion recommendations. These ambiguities arise from general statements to include ‘patient information’, ‘primary care provider information’ and ‘clinical information’ which were not specifically defined.

Table 1
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Summary of Canadian referral guidelines separated into categories and specific referral data that were recommended to include in referrals

Characterisation of referral data fields on clinically used referral forms

A total of 622 documents were collected from OSCAR EMR, SWPCA and Ocean eReferral (online supplemental table S2). Four hundred and fifteen documents remained after excluding 9 administrative documents, 8 clinical tools, 6 COVID-19 resources, 163 diagnostic imaging forms, 7 duplicates, 10 laboratory forms, 1 long-term care application and 3 patient information sheets (online supplemental table S2). The included referral form represented 42 different specialties (online supplemental table S2). The 150 randomly selected forms represented 32 different specialties (table 2) and had representation from all geographic areas within Ontario, Canada (online supplemental table S3).

Table 2
|
Distribution of specialties and number of referral forms randomly selected for review

Review of the 150 randomly selected referral forms identified 264 unique types of referral data that were requested by consultant providers (table 3). This means that consultants requested 222 more unique referral data types than were identified in the referral guidelines (table 3). Additionally, 23 types of social history referral data were requested, which were not included in any referral guidelines (table 3). Administrative referral data were limited in the referral guidelines but were frequently requested by consultant providers. Referral data fields were classified into four different formats, information for referring providers, checkboxes, free-text entry or attachment requests. A full list of all referral data identified in the referral forms is available in online supplemental table S4.

Table 3
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Categories of unique referral data on referral forms and in referral guidelines

The average number of referral data fields per digital referral was significantly higher than paper referrals (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01; figure 1). Subgroup analysis (figure 2) demonstrated that digital referrals have significantly higher average number of informational data (13.7±1.7 vs 8.6±2.9; 95% CI p<0.01), checkboxes (13.7±1.7 vs 4.8±3.4; 95% CI p<0.01) and free-text entry (31.1±5.0 vs 16.1±6.0; 95% CI p<0.01) requests. The average number of attachment requests was not significantly different between digital and paper referrals (1.1±1.4 vs 1.0±1.0; 95% CI p=0.38).

Figure 1
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Figure 1

Mean number of referral data types requested per paper and digital referral form (***p≤0.01).

Figure 2
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Figure 2

Mean number of referral data types requested within each category for paper and digital referral forms (***p≤0.001).

Codesigned referral form

A total of 29 and 21 participants attended the first and second codesign sessions, respectively (table 4). During the first session, participants decided on which referral data were included in the standardised referral form (table 5). Codesign design participants indicated that some specialties may need custom referral forms with additional information requests (online supplemental table S5). The customisations identified during the codesign sessions included options to select specific providers or locations, referral eligibility criteria, required prereferral testing, disease-specific clinical guidance, unique data to triage appropriate clinic location and specific criteria to triage referral priority. During the codesign discussion, participants expressed that customisations would help referring providers provide better referral, improve patient care and reduce unnecessary or inappropriate referrals.

Table 4
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Categorisation of codesign participants by codesign session
Table 5
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Codesigned, standardised, generic referral form data template. When possible, ‘yes’ and ‘no’ questions were formatted so that a checked box indicates a ‘yes’ response

The codesign participants also provided several recommendations to improve referral form quality beyond the specific referral data. Participants recommended that referral forms be brief and minimise administrative burdens, leverage electronic medical records to auto-complete referral forms, only ask for referral data that referring providers will reasonably have, and avoid collection of referral data that do not facilitate referral triage and eligibility decision-making. Codesign participants recommended that if consultant providers require more detailed information, then this could be collected either prior to the initial consultation via a patient completed intake questionnaire or during the initial consultation.

Codesigned referral form user experience

A total of 147 referring providers that sent referrals using the codesigned referral form were contacted to provide feedback on their experience. Eighteen responses were received from providers in three different regions of Ontario (Central, Toronto and West). All 18 responses were from providers working in primary care clinics. Of those who responded, 11 (61%) were primary care physicians, 4 (22%) were nurse practitioners, 1 (6%) was an allied health practitioner, 1 (6%) was an office administrator and 1 (6%) was a referral clerk. Respondents rated their experience using the codesigned referral form positively, with 14 of 17 respondents rating their experience as excellent or good (2 excellent and 12 good).

Discussion

Referring providers have been criticised for writing poor-quality referrals for many years.1–3 This study identified inconsistencies and ambiguities within Canadian referral guidelines. No referral data type was consistently identified for inclusion by all referral guidelines. Surprisingly, only 7 of 13 (54%) referral guidelines specifically recommended the patient’s name and 8 of 13 (62%) recommended the patient’s contact information, be included in the referral. The remaining guidelines either made no recommendation21 or generally specified ‘patient information’.22 23 We propose that the lack of consensus and specificity in referral guidelines contributes to why referring providers unintentionally omit essential referral data, leading to low-quality referrals.

To our knowledge, this study presents the first characterisation of referral data fields from clinically used referral forms across multiple specialties. The requested referral data should contain all clinically relevant information that consultants need.24 We, therefore, used this data to codesign a standardised referral form with referring providers, consultants and administrators as recommended by the Canadian Medical Association.8 Codesign participants wanted referral forms to be short and simple; however, this study demonstrated that newer digital referral forms requested more information from referring providers. One potential reason why digital referral forms are longer and more complex is because digital referrals are not restricted to a single physical sheet of paper. Limiting digital referral form length is important because additional referral data does not correlate with consultants’ confidence in triaging appointments.25 Referral form length will also increase administrative burdens for referring and consultant providers, which correlates with provider burnout13 14 and intention to stop practicing.15 Therefore, we recommend that consultants’ providers adopt shorter, standardised, evidence-based referral forms, such as the one codesigned here.

The codesign of this referral form is a step toward providing clearer referral guidelines to improve referral quality. Some studies have recommended that referring providers require more training on how to write referrals,4 26 however, a Cochrane review from 2008 identified that education alone is insufficient.27 Instead, we recommend following England’s National Health Services’ Sustainability Model, creating interventions that target processes, staff education and organisational improvements.28 The Cochrane review concluded that clear referral guidelines (staff education) released in conjunction with a referral form (process change) can significantly improve referral quality.27

A randomised trial in Norway also demonstrated that a combination of provider education and referral form improves referral quality.29 However, the Norwegian referral form was based on disease-specific clinical guidelines and consultant opinion. The codesign approach used in this study facilitated dialogue between referring providers, consultant providers and administrators, leading to more nuanced learning. Specifically, the codesign participants highlighted that specifying referral data fields is only one part of the problem. Participants expected high-quality referral forms to be brief, reduce administrative burdens, leverage technology to facilitate form completion and only request referral data that facilitate consultant triage. These finding came organically from the codesign open forum and there is increasing awareness of the value that codesign brings to digital health technology development.30 Given these findings, it is possible that previous efforts to standardise referral forms have failed because without codesign, referral forms tend towards being longer, more complex and request information that referring providers do not have or is better collected directly from patients.

The next step from this study is to further implement the codesigned referral form in clinical practice. Additionally, we recommend revision to existing referral guidelines to provide clearer direction for referring and consulting providers. Once this is completed, then further quality improvement cycles may be completed to further refine and define the components of high-quality referrals and referral forms.

Strengths and limitations

The main limitation of this study was that all reviewed referral forms and codesign participants were from Ontario. We attempted to mitigate any local practice patterns by collecting forms from all geographic regions within Ontario. However, there may be difference in the referral data that consultants require in different regions. Codesign participants also expressed this concern and suggested that the codesigned referral form could be customised for different regions as needed to assist with referral triage or decision-making. Additionally, this study only assessed referral forms for consultation requests. Accordingly, these findings and the codesigned referral form will not be adequate for all referral types, such as diagnostic, home care, allied health or laboratory services. Finally, the referral form review was completed by a single author due to study constraints. However, each codesign participant had the opportunity to review and discuss these findings, which should mitigate potential biases in the primary outcome—the codesigned referral form.

Conclusion

This study has demonstrated that referral guidelines lack consistency and specificity, which may contribute to poor-quality referrals. To our knowledge, this is the first study that has characterised the referral data requested by consultant providers. These data were used to codesign a referral form with referring providers, consultant providers and administrators, which should be adequate for consultation referrals across multiple specialties. Implementation of this codesigned referral form is expected to improve referring providers’ experience by reducing administrative burdens and improve referral quality by more clearly defining essential referral data fields. Further studies will be needed to assess and improve the codesigned referral form’s impact on referral quality, referral appropriateness, patient safety, and provider experiences.

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