Developing a preconception medical record audit tool for general practice: a multimethod study
Nishadi N. Withanage A B * , Jessica R. Botfield A B , Sharon James A B , Kirsten I. Black B C , Sharon Cameron D Danielle Mazza A BA
B
C
D
Abstract
Preconception health risk factors documented in general practice electronic medical records help general practitioners (GPs) understand the proportion and type of risks among women attending their practice. We aimed to collaborate with GPs to develop an audit tool for collecting preconception health data from structured electronic medical record fields.
This was a three-phase multimethod study. In Phase 1, we developed a preliminary audit tool informed by the literature. In Phase 2, we collaborated with GPs for feedback. In Phase 3, we finalised the audit tool.
The preliminary audit tool comprised 25 preconception health risk factors, of which three were removed following GP feedback (second-hand smoke, social history, history of sexually transmitted infections). The final audit tool comprised 22 preconception health risk factors.
This audit tool may assist researchers in understanding the proportion of patients visiting general practice with preconception health risk factors, thereby facilitating the future development of a screening process that may be used to identify and provide preconception care to women who may most benefit from it.
Keywords: audit, general practice, medical records, preconception care, pre-pregnancy care, primary care, reproductive age, women.
Introduction
Preconception care (PCC) involves counselling, and the provision of biomedical, behavioural and social health interventions to optimise the health of reproductive-aged (18–44 years) women and their partners before pregnancy (Robbins et al. 2018). These interventions, which can be integrated into primary care (van Voorst et al. 2016), community (Poels et al. 2018) and hospital settings (Shannon et al. 2014), address medical and lifestyle risk factors, including obesity, diet, excessive alcohol consumption and smoking, which may increase the likelihood of adverse pregnancy outcomes (Ray et al. 2001; Temel et al. 2014; De-Regil et al. 2015; Schummers et al. 2015; Barker et al. 2018). Optimising preconception health reduces maternal and infant mortality and morbidity, and improves the overall health of the mother and baby (Dean et al. 2014).
A systematic review we conducted on the effectiveness of PCC interventions in primary care highlighted that PCC reduces risk factors, such as smoking and excessive alcohol consumption, while improving health knowledge among reproductive-aged women (Withanage et al. 2022). Therefore, exploring strategies to routinely provide PCC in primary care, such as general practice, is warranted to improve maternal and fetal health, and reduce adverse pregnancy outcomes.
In Australia, GPs provide numerous preventive care services, including alcohol and smoking management, cervical screening, chlamydia screening, and vaccination (Mazza et al. 2013). The largest barrier to PCC provision was competing priorities in time-restricted consultations, particularly as PCC often requires more time than a standard consult. To improve PCC provision, clinicians working in general practice should be aware of the patient’s risk profiles, particularly for those at higher risk of adverse pregnancy outcomes. Currently, it is unclear which preconception health risk factors are documented in general practice electronic medical records (EMRs) and to what extent.
EMRs are a rich source of patient data that can enhance preventive health (Thuraisingam et al. 2021), containing information, such as medications, past medical history, family history, obstetric history, immunisations, bodyweight, height, body mass index (BMI), alcohol consumption and smoking (Thuraisingam et al. 2021). However, descriptive text in clinical notes and pathology reports (Thuraisingam et al. 2021) can be difficult to access and extract, requiring significant manual effort and time (Mazza et al. 2013; Tayefi et al. 2021). Therefore, collecting data from structured fields in EMRs may be a more accessible approach (Turner et al. 2015, 2017).
Data relating to preconception health risk factors in structured fields in EMRs can help identify patients at risk of adverse pregnancy outcomes, allowing clinicians, such as general practitioners (GPs) and general practice nurses (PNs), to offer targeted preventive care and PCC. However, the documentation of these risk factors in general practice has not been standardised internationally. Although digital preconception health assessment tools have been developed in some countries (Landkroon et al. 2009; Batra et al. 2018; Montanaro et al. 2023), no gold standard tool exists for preconception health data collection (Montanaro et al. 2023).
Collaborating with end-users maximises research outcomes, and benefits both researchers and clinicians (Sanders and Stappers 2008; Yuan et al. 2015). This study aims to develop an audit tool for preconception health data collection from general practice EMRs by working with GPs, key PCC providers and experienced EMR users in Australian general practice (Dorney and Black 2018; Canaway et al. 2019; Monaghan et al. 2020). Previous studies have shown the value of collaborating with GPs to design tools, such as electronic disease quality improvement tools (Hunter et al. 2020), a patient safety guide (Morris et al. 2021) and an artificial intelligence documentation assistant (Kocaballi et al. 2020), to improve services (White et al. 2023). In this study, we aim to collaborate with GPs to develop a tool of preconception health risk factors that can then potentially be used to understand how commonly preconception health risk factors are documented in structured fields in EMRs and the burden of preconception risk in a given patient or practice.
Methods
This study was conducted in Melbourne, Victoria, Australia, from 2022 to 2023, and consisted of three phases. Phase 1 involved the development of a preliminary tool; comprising potentially auditable preconception health risk factors that can be found in EMR structured fields, informed by the literature (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022). Phase 2 involved collaborating with GPs working in Australian general practice to obtain their feedback on the preliminary tool (Table 1). Semi-structured interviews with GPs were conducted to obtain end-user (GP) contributions (Slattery et al. 2020). Phase 3 involved finalising the tool following GP feedback. Written informed consent for participation in this study was obtained from the participating GPs before conducting the interviews.
| Age | |
| Alcohol consumption | |
| Smoking status | |
| Second-hand exposure to smoke | |
| Recreational drug usage | |
| History of eating disorder(s) | |
| Height (cm) | |
| Weight (kg) | |
| Body mass index (BMI) | |
| Folic acid and iron supplementation | |
| Physical activity | |
| History of sexually transmitted infections | |
| Thyroid disease | |
| Mental health illness | |
| Asthma | |
| Medications with potential teratogenic effects: ACE (angiotensin converting enzyme) inhibitors, angiotensin II antagonist, Isotretinoin (acne), lithium, Warfarin, Meloxicam, spironolactone (diuretic), Coumadin (anticoagulant), tetracycline (acne), valproic acid (epilepsy) | |
| Blood pressure | |
| Social history | |
| Family history | |
| Obstetric history | |
| Immunisations | |
| Genetic diseases | |
| Fertility problems | |
| Diabetes | |
| Blood glucose (mmol/L) |
A similar method to our study, involving a literature review and expert input, has been used to develop audit tools/checklists, such as a tool for assessing components in combined lifestyle interventions for children with overweight and obesity (Saat et al. 2023), a novel instrument for characterising telemedicine programs in primary care (Cho et al. 2023), and a framework for capturing research impact in nursing and midwifery (Newington et al. 2023).
Data collection
To compile our preliminary audit tool, we read and extracted information on preconception health risk factors from three key publications (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022). These publications were specifically chosen, as they outline a comprehensive list of preconception health risk factors (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022).
Guidelines for preventive activities in general practice (Red Book; RACGP 2020); the Royal Australian College of General Practitioners has published the Red Book since 1989 to support evidence-based preventive activities in primary care. The Red Book is now widely accepted as the main guide for the provision of preventive care in Australian general practice. It provides a comprehensive and concise set of recommendations for patients in general practice with additional data about tailoring advice depending on risk and need (RACGP 2020).
Review of national population-level preconception health indicators (Schoenaker et al. 2022); this review of national population-level preconception health risk factors identified 66 indicators across 12 domains that can be used to assess and determine the population’s preconception needs, improve patient care, and inform and evaluate new campaigns and interventions that enhance preconception health (Schoenaker et al. 2022).
An article recommending clinical areas of focus to address during PCC delivery (Jack et al. 2008); this publication provides a summary list of recommendations representing evidence-based best practices in PCC (Jack et al. 2008). As the preconception health indicators documented in these three publications (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022) were not specific to general practice EMRs, we compiled a preliminary list of risk factors that may be documented in general practice EMRs, through discussion among the research team; comprising GPs, women’s health clinicians and researchers with experience in women’s sexual and reproductive health in primary care.
We recruited GPs from our professional networks and the Centre of Research Excellence in Women’s Sexual and Reproductive Health in Primary Care (SPHERE CRE) GP Advisory Circle to participate in semi-structured interviews via Zoom videoconference to review and provide feedback on the preliminary audit tool. The SPHERE GP Advisory Circle meets on an as-needed basis to provide discipline-based advice and feedback into SPHERE research studies relevant to primary care. Prospective GPs were invited via email to participate and were provided with an explanatory statement detailing the aims of the study. GPs who expressed interest in participating were sent a written informed consent form to complete and return. Verbal consent was also obtained at the start of the interview. A semi-structured interview guide (Box 1), developed and piloted by the research team, was used for all interviews. Interviews were conducted between May and June 2022 at a time convenient for the GP and the interviewer (NW; BBiomedSc Adv Hons, PhD candidate, female). The interviewer shared her computer screen and presented the GPs with the preliminary audit tool (Table 1) developed in Phase 1 for feedback. All participating GPs received a A$150 e-gift card. To refine the preliminary audit tool, the interviewer made field notes during the interview, relating to the feedback provided by participating GPs.
Interviews were audio recorded following participants’ verbal consent and professionally transcribed verbatim (Guest et al. 2006). Transcripts were quality-checked for accuracy and de-identified by NW. These were not returned to the participants for review. The interview transcripts were coded using NVivo 14 software by NW to extract relevant data to refine and finalise the audit tool.
Phase 3 involved finalising the audit tool after GP feedback. Following feedback from GPs, we developed the final audit tool. Participating GPs did not provide feedback on the final tool.
Ethics approval and consent to participate
Ethics approval was granted by the Monash University Human Research Ethics Committee (Project ID 31487), and the study was conducted in accordance with the Australian Code for the Responsible Conduct of Research. All general practitioners who participated in the study provided written informed consent.
Results
Phase 1 involved the development of a preliminary audit tool
Following the review of the Red Book (RACGP 2020), the review of national population-level preconception health risk factors (Schoenaker et al. 2022) and the publication reviewing topics for PCC (Jack et al. 2008), we compiled a list of preconception health risk factors that can be extracted from structured fields in EMRS for inclusion in our preliminary audit tool (Table 1). Among the ‘preconception: preventive interventions’ mentioned in the Red book (RACGP 2020), we included all preconception health risk factors, except those unlikely to be found in a structured field in general practice EMRs, such as those relating to interpregnancy intervals and oral health. Although the review of national population-level preconception health indicators (Schoenaker et al. 2022) and the publication recommending topics for PCC (Jack et al. 2008) provided an extensive list of preconception health risk factors, we included only those that were likely to be documented in structured fields in general practice EMRs.
Phase 2 involved collaborating with GPs to obtain their feedback on the preliminary audit tool
Seven GPs provided informed consent to participate in a semi-structured interview to provide feedback on the preliminary audit tool (Table 1). As there were no new themes after six interviews, we only conducted seven interviews. All were female; two worked in practices in areas of low socioeconomic status and four worked in practices in areas of high socioeconomic status; and all but one worked in a practice in an urban area. Five GPs were from Victoria, one was from New South Wales and one was from the Australian Capital Territory. The mean duration of the interviews was 27 min and the findings are summarised in Table 2. Following the completion of interviews, the preliminary audit tool was refined and finalised.
| GPs feedback | Quotes relating to GPs feedback | |
|---|---|---|
| Exclude social history and second-hand smoke exposure, as there are no structured fields in EMRs for documenting these risk factors | ‘I don’t think social factors will be recorded. Family violence, abuse, that kind of thing would not be in the practice record’ GP03 ‘…second-hand exposure to smoking is generally something that’s not identified, as there is not a specific spot to put it in the software’ GP04 | |
| Data relating to blood glucose levels may be found in the pathology reports, although there is a structured field in EMRs, it is often not recorded there | ‘… if I was looking in a patient’s file I would have to go and look at what investigations they have had done, and go through and find a blood test where the blood glucose level is on it.’ GP04 | |
| Exclude STIs, as that is usually recorded in clinical notes | ‘You would not want to put STIs in the past medical history, because you don’t want STIs to appear in a letter saying the patient has for example chlamydia.’ GP06 | |
| Instead of reviewing EMRs to find specific medications that potentially have teratogenic effects, record all prescribed medications in the audit tool and then categorise according to the Australian categorisation system (TGA 2024) for prescribing medications during pregnancy | ‘It’s not just teratogenic medication, sometimes you can have medication that’s Category C or D’ GP02 ‘…teratogenic effect is tricky, because you’ll have your classes from A to D. D is obviously teratogenic, but then you really wouldn’t want to prescribe someone a category C medication.’ GP03 ‘I would just write down any medications that they are on – because there could be multiple other things that you don’t have on your list that shouldn’t be used.’ GP06 | |
| Data documented for height, weight, BMI, alcohol consumption, blood pressure, smoking and family history may be outdated | ‘I don’t routinely – I must say I don’t routinely take the weight of my patients unless I’m concerned about their weight or they’re concerned about their weight or we have a medical condition that is of concern to their weight.’ GP01 |
In reviewing the preliminary audit tool (Table 1), GPs agreed that the following factors are important preconception health risk factors and should be retained in the audit tool: age, alcohol consumption, smoking status, recreational drug usage, history of eating disorders, height, weight and BMI (within the last 24 months), folic acid and iron supplementation, physical activity, thyroid disease, mental health illness, asthma, current medication, blood pressure (within the past 24 months), family history, obstetric history, immunisations, genetic diseases, fertility problems, diabetes, and blood glucose (within the past 24 months).
However, there were several preconception health risk factors that GPs felt would be unlikely to be documented in a structured field in EMRs and should therefore not be included in the final audit tool, as described below (Table 2). GPs suggested that data relating to social history and second-hand exposure to smoke may not be documented in a structured field in EMRs. Safety at home and family violence were identified as important social risk factors for preconception health; however, it was also noted that there was no specific structured field in the EMR to document these data.
For other factors related to clinical investigations, such as blood glucose levels, although there may be a related structured field, GPs reported that in most cases these data are contained in pathology reports and not documented in the structured field.
It was also mentioned that data relating to sexually transmitted infections (STIs) might be documented in clinical notes rather than in a structured field, as GPs would not want data appearing in a letter or being passed on to an insurance company.
Regarding medications, GPs expressed concerns about their limited awareness of all the medications their patients are using, especially when prescribed by other healthcare providers. As a result, the medication information documented in the EMR may not accurately reflect the actual medications the patient is currently using. With regard to medication, GPs also proposed documenting all medication in the tool and then categorising it according to the Australian categorisation system (TGA 2024) for prescribing medications during pregnancy, noting that drugs classified into categories C, D or X may have harmful effects on the human fetus (Schirm et al. 2004).
All GPs stated that preconception health risk factors, including height, weight, BMI, alcohol consumption, blood pressure, smoking and family history, are not routinely updated for patients unless needed. Consequently, data relating to these risk factors may therefore have been collected at an initial consultation, but not subsequently updated and, therefore, may not be recent.
Phase 3 involved finalising the audit tool following GP feedback
Following interviews with GPs, we developed the final audit tool (Table 3). As GPs emphasised that second-hand exposure to smoke, data relating to STIs and social history may not be documented in a structured field in the EMR, these factors were removed from the final audit tool. Furthermore, in the preliminary audit tool, we only listed some teratogenic medication (i.e lithium, warfarin, tetracycline and valproic acid), based on specific teratogenic medication mentioned in the publication recommending topics for PCC (Jack et al. 2008). Given category C, D and/or X medication may have harmful effects on the human fetus (Schirm et al. 2004), as suggested by GPs, we updated the tool to include any ‘current medication’ to then categorise results according to the Australian categorisation system (TGA 2024) to enable identification of patients taking category C, D and/or X medication. Finally, as weight, BMI, blood pressure and blood glucose data might not be recent, we determined that unless these measurements have been updated within the past 24 months, it may not be suitable to rely on these measures for evaluating an individual’s preconception health. Similar approaches have been utilised in a study examining cardiovascular disease risk factors (Turner et al. 2017) and a study investigating the documentation of BMI (Turner et al. 2015).
| Age | |
| Alcohol consumption | |
| Smoking status | |
| Recreational drug usage | |
| History of eating disorder(s) | |
| Height (cm) | |
| Weight (kg) | |
| Body mass index | |
| Folic acid and iron supplementation | |
| Physical activity | |
| Thyroid disease | |
| Mental health illness | |
| Asthma | |
| Current medication | |
| Blood pressure (within last 24 months) | |
| Family history | |
| Obstetric history | |
| Immunisations | |
| Genetic diseases | |
| Fertility problems | |
| Diabetes | |
| Blood glucose (mmol/L) (within past 24 months) |
Discussion
Summary
We developed an audit tool for collecting data on preconception health risk factors documented in structured fields in general practice EMRs. Through a review of relevant publications (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022) and collaborating with GPs, we identified 22 preconception health risk factors likely documented in structured fields within EMRs.
When refining the preliminary audit tool, we excluded preconception health risk factors that were less likely to be documented in structured fields. For example, despite STIs, social history and second-hand exposure to smoke being significant preconception risk factors (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022), there are no structured fields for recording these data in EMRs. GPs have identified time constraints (Mazza et al. 2013) as a barrier to providing PCC, hence reviewing free-text notes, which are not easily searchable and may not support clinical decision support (Brown et al. 2014), may place a further burden on already time-restricted GP consultations. Therefore, this audit tool only encompasses preconception health risk factors for which data can be extracted from structured fields.
Reproductive-aged women may receive medications from their primary care clinicians that can cause birth defects if used during pregnancy (Donald 2024). Primary care clinicians often face challenges in identifying patients who may become pregnant and need teratogenic risk counselling. Approximately 26% of pregnancies in countries such as Australia are unintended (Taft et al. 2018), leading some women to use teratogenic medication during pregnancy, which can result in adverse pregnancy outcomes. Therefore, the final audit tool was modified to extract data on all prescription medications, categorising and evaluating them according to the Australian categorisation system (TGA 2024) to determine any risks associated with taking particular medicines on pregnancy outcomes.
Although preconception health risk factors documented in EMRs can help identify women at risk of adverse pregnancy outcomes (Atlass et al. 2020; Bello et al. 2022), the recency of the data is crucial. For example, smoking and alcohol consumption data collected years ago may not be relevant for current assessments (Floyd et al. 2008). However, as EMRs lack date options for recording alcohol and smoking data, we cannot extract data recorded for these within a specified period. Because our audit tool includes indicators, such as height, weight, BMI, blood pressure and blood glucose, which have recorded dates, we decided to extract data updated within the past 24 months. This decision was based on feedback from GPs, and similar approaches in studies on cardiovascular disease risk factors (Turner et al. 2017) and BMI documentation in Australian general practice (Turner et al. 2015).
Previous studies have effectively used audit tools to accurately identify patients with specific conditions using EMR data (Xu et al. 2011; Peiris et al. 2013; Rahimi et al. 2014; Tu et al. 2014; Elliott-Rudder et al. 2017; Smeets et al. 2020). For example, an audit tool developed for improving cardiovascular disease risk screening in general practices detected most chronic disease data in EMRs (Peiris et al. 2013). Similarly, a study in regional Australia that audited EMR data to identify rates of osteoporosis diagnosis identified osteoporosis in 20.9% of the patients visiting the general practice (Elliott-Rudder et al. 2017). Using a similar method to ours, in another Australian study, researchers developed an ontology-based algorithm to extract data from EMRs to identify patients with type 2 diabetes (Rahimi et al. 2014). The algorithm, validated using general practice EMR data, accurately identified type 2 diabetes patients, albeit compromised by incomplete data (Rahimi et al. 2014). These findings are consistent with international studies that used EMR data to identify patients with heart failure, (Smeets et al. 2020) epilepsy (Tu et al. 2014) and for colorectal screening (Xu et al. 2011). Despite potential gaps, evidence supports the role of EMRs in enhancing the quality of care and patient management (Manca 2015), highlighting the need for audit tools like ours to identify at-risk patients and enable more targeted care, especially to those with existing risk factors.
This study has both strengths and limitations. Based on GP feedback, recording all medication data documented in the structured fields could enable the identification of category C, D and/or X medications that may harm the fetus. Collaborating with GPs was crucial, given their daily use of EMRs and familiarity with the documented data (McInnes et al. 2006). Despite the small sample size of interviewed GPs, it was sufficient to provide feedback on the preliminary audit tool. All the GP participants were working intensively in women’s health and only one was rural. Due to the variability in EMR software packages used in Australia, the experiences of GPs using the different software packages may differ and we excluded some preconception health risk factors as they thought it may not be recorded in the EMR. It would have been beneficial to have GPs working in remote, rural and regional areas and GPs without a special interest in women’s health. However, our tool is limited to capturing preconception health risk factors present in structured fields in EMRs, and it may not account for relevant data documented as free-text notes.
Our collaboration with GPs resulted in a pragmatic tool focused on recently documented preconception risk factors in structured EMR fields. Feedback from PNs would have also been beneficial, as they also use EMRs and may be involved in providing preventive care, including during preconception. As shown in previous studies (Peiris et al. 2013), once the limitations of this study are addressed, this tool can be used to conduct a manual audit to understand the proportion of women with preconception health risk factors visiting general practices. This may assist GPs and PNs in identifying those women in a practice population most susceptible to adverse pregnancy outcomes, to increase their engagement in PCC. Future studies should investigate GPs’ and PNs’ perceived acceptability and feasibility of using the preconception health risk factors in this tool to identify women susceptible to adverse pregnancy outcomes in general practice. If deemed acceptable and feasible, data extraction tools in Australia, such as the Population Level Analysis and Reporting screening tool (Haas et al. 2021), Pen Computer Systems Clinical Audit Tool and Precedence’s Coordinated Care Platform (Canaway et al. 2019), can be used to search for and filter women with preconception health risk factors to enhance PCC provision. Additionally, incorporating structured fields for data on second-hand smoke exposure and STIs could be considered (Jack et al. 2008; RACGP 2020; Schoenaker et al. 2022). To further assist GPs in PCC provision, EMRs could also be modified to quantify preconception health risk factors to produce a risk score, as done for cardiovascular risk stratification.
Conclusion
Our audit tool may aid researchers in gathering preconception health data from EMR-structured fields. This approach may assist researchers in understanding the proportion of women with preconception health risk factors visiting general practice, aiding in the development of interventions and screening processes to identify women who may most benefit from PCC.
Declaration of funding
This study was funded by a SPHERE seeding grant. SPHERE is a NHMRC Centre of Research Excellence in Sexual and Reproductive Health for Women in Primary Care that aims to improve the quality of and access to sexual and reproductive healthcare services to women in Australian primary care.
Author contributions
All authors were involved in the conceptualisation and methodology development aspects of this study. NW led the investigation with support from JB, SJ, KB, SC and DM. NW led the writing of the original draft. All authors reviewed, edited and approved the final manuscript.
Acknowledgements
We thank SPHERE for funding this project. We also thank all the GPs who contributed their valuable time and support for this project.
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