Integration of a patient-orientated eHealth intervention in the setting of an established enhanced recovery after surgery program can reduce complications and length of stay: an observational study

Introduction

New Zealand has the highest incidence of colorectal cancer (CRC) in the world for women and the second highest for men (1). Of those diagnosed with CRC in New Zealand, 63% will undergo curative surgery. Complications from surgical treatment of CRC occur in approximately one third of patients (2-4), resulting in significant patient morbidity, an increase in length of stay (LOS), and increased cost to the health care system (5,6).

The enhanced recovery after surgery (ERAS) initiative significantly improves postoperative outcomes in colorectal surgery, reducing complications (especially non-surgical complications) by over one third, and reducing length of stay without increasing readmission rates (7,8). ERAS initiatives include a package of evidence-based interventions covering preoperative, intraoperative, and postoperative care. These include patient education, optimising preoperative nutrition and function, infection prevention, limiting the stress response to surgery, multimodal analgesia and anti-emetics, judicious management of intravenous fluids, avoiding unnecessary use of drains and tubes, and early postoperative nutrition and mobilisation. Optimal patient engagement is achieved when ERAS principles are regularly reinforced and followed by both health care providers and the medical institution providing care. Lack of emphasis and education about ERAS principles, including not optimising patients preoperatively or not emphasising the implementation of ERAS targets and guidelines postoperatively (9), can limit the effectiveness of ERAS programs (10). This may be exacerbated by resource constraints and staff shortages present in some public hospitals. This led us to develop content for an eHealth intervention to augment an existing ERAS program and examine the impact of its use when compared to standard care.

eHealth, defined by the World Health Organization as “the use of information and communication technologies for health”, is emerging as a supplement to, or as a more effective way of providing, standard care in surgery (11). Patient monitoring or education are common elements of eHealth platforms across a range of surgical specialities, including colorectal surgery (12-14). Perioperative eHealth platforms in the current literature are mainly designed to support post-discharge recovery rather than pre-operative optimisation (15). There is minimal data regarding the role of eHealth in supplementing and supporting ERAS programs and what impact this may have on complications.

The Go Well Health (GWH) platform is a modular, online, patient-facing educational eHealth software platform produced by SHI Global (16). Colorectal content, developed collaboratively by our clinical staff, covers prehabilitation, hospital, and post-discharge care. This includes bite-sized packages of information about pathology, surgery, risk of complications, and evidence-based steps that can be taken to mitigate the risk of complications. The hospital’s ERAS program, including the principles of ERAS and the use of postoperative targets, is explained. Postoperative exercises, including respiratory and strength exercises to help with mobilisation, are explained in PDF and video format, enabling them to be practised preoperatively. Daily postoperative targets are provided and monitored by an ERAS nurse. The platform also provides interactive elements, including questionnaires, pre-operative checklists, the ability of patients to contact the clinical team, and checks on progress after discharge. These facilitate communication between patients and their health care team. We present this article in accordance with the STROBE reporting checklist (17) (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-41/rc).

Methods

This is a retrospective, observational cohort study comparing post-operative clinical outcomes of patients undergoing elective colorectal resection for neoplasia who use an eHealth application or undergo standard care at Dunedin Hospital. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The University of Otago (No. H24/0070) and individual consent for this retrospective analysis was waived.

Recruitment

Adult patients undergoing elective laparoscopic or open colorectal resection with a diagnosis of a benign or malignant neoplasia between 1/1/2020 and 1/4/2024 were included in the study. Patients having surgery for recurrent malignancy or non-neoplastic disease, and those having surgery without a resection, such as a surgical bypass, were excluded.

To ensure that the eHealth and standard care groups were similar, patients who would not have been offered the eHealth intervention were also excluded from the control group. This included patients having transanal excisions for benign disease and patients being referred from out of town with polyposis syndromes but no malignancy, as patients with likely benign disease were not usually seen preoperatively by the colorectal nurse specialist (CNS). Included patients had complete data for variables of interest.

The eHealth intervention has been offered to patients by a CNS and the program co-ordinator at Dunedin Hospital since 2020. Patients met with a CNS shortly after the diagnosis of CRC. As part of the package of care, they were advised about the eHealth intervention. They would then be contacted by the program co-ordinator to decide whether they would opt for the eHealth intervention or standard care.

Since 2012, all patients received preoperative, intraoperative, and postoperative care according to our institutional ERAS protocol, which is based on the ERAS Society guidelines for elective colorectal surgery. This is delivered by multidisciplinary clinical teams and a dedicated CNS. A previous publication (9) demonstrates Dunedin Hospital’s high compliance rates for ERAS recommendations in patients undergoing elective colorectal surgery at Dunedin Hospital.

The eHealth intervention is described in the introduction. This could be accessed from any computer or mobile device with IOS or Android operating systems. Patients could also invite a friend or relative onto their program, who then had access to the online content, enabling them to discuss any advice or recommendations.

Statistical analysis

Data for this audit are drawn from two prospectively populated databases used for surgical patients in Dunedin Hospital: the Otago Clinical Audit (OCA) (18) and the GWH database. Surgical admissions are automatically uploaded into the OCA database, clinical staff complete surgical details in theatre, and complications are documented at the time of discharge from the hospital. These are coded by the treating team and signed off by the consultant in charge of that patient’s care. Data retrieved from OCA included patient characteristics (sex, age at time of surgery) and clinical data such as the American Society of Anaesthesiologists (ASA) score, discharge date, relevant diagnoses, operative procedures, timing of procedures, length of hospital stay (LOS), complications, and readmission to hospital. The GWH database is generated from real-time interaction with patients and monitoring of online activity. Data retrieved included time spent on the eHealth platform, number of uses of the eHealth platform, and satisfaction ratings of the eHealth content. Interactions less than 15 seconds were excluded as likely not meaningful. Similarly, interactions longer than one and a half times the third quartile were removed to reduce measuring erroneous use due to content not being closed after it had been viewed.

The main clinical outcomes were complications, length of stay, and readmissions. Complications were defined in line with National Surgical Quality Improvement Program (NSQIP) definitions (19), although there was also freedom for clinical staff to document other adverse events impacting on patient care. Identically reported complications recorded for the same patient on more than one occasion were counted as one complication. The number of patients with a complication and the total number and type of complications were reported. Length of stay was determined by the number of days in hospital—day 0 being the day of surgery. Readmission to hospital was documented up to 30 days after surgery.

Statistical analysis was performed with R 4.4.0. Categorical data was reported as the number and percentage (%). Continuous data with a normal distribution was summarised using the mean and standard deviation, and not normally distributed data was described with the median and interquartile range. Differences in number of complications were evaluated using the Pearson’s Chi-squared test with Yates’s correction for smaller numbers and the Fisher’s exact test for cell counts less than five. The presence of any complication between the groups was evaluated with a general linear model with binomial distribution, including age and ASA to assess for any associations with these variables. Differences in ASA categories were evaluated with the Cochrane Armitage trend test. Non-normally distributed continuous data (LOS, age, number of complications) were evaluated using the Wilcox Rank Sum test. Propensity score matching was performed for operative procedure, age, and ASA. This used a one-to-one nearest match propensity scoring method (20) for age and ASA score, and exact match propensity scoring for resection type. The Quality of propensity matching was measured using standardized mean difference (SMD). Intention-to-treat (ITT) and per-protocol (PP) analyses were performed. ITT analysis included all patients in the eHealth group. PP analysis removed patients who were not adherent or did not have a meaningful interaction with the eHealth platform, which was defined as total interaction of a duration less than 15 seconds. Statistical significance was set at P≤0.05.

Results

We identified 333 patients undergoing elective colorectal surgery at Dunedin Hospital meeting the inclusion criteria. One hundred and two patients used the eHealth platform. There was no documentation of the number of patients who were not offered the use of the platform or of those who declined to use the platform. Non-eHealth-using patients were a median of 3 years older than eHealth-using patients at the time of surgery (P=0.02). There were no significant differences in sex, operation (resection type), or ASA. The propensity-matched cohort comprised 102 patients who were well-matched for sex, age, resection type, and ASA score (Appendix 1). Characteristics of patients before and after propensity matching can be seen in Table 1. SMD showed suitable matching between the groups, with the SMD ≤0.1 for all covariates (Table S1).

Good adherence for using the eHealth application was present in 92 of 102 patients (90%). Patients engaged with the platform for a median of 125 minutes [interquartile range (IQR), 48–261 minutes] over 69 interactions (IQR, 22–104 interactions). The majority of interactions were rated as “really helpful” (4,639, 72%) or okay (1,693, 26%), with less than one percent being not helpful. Eight patients that signed up to the eHealth platform did not open the platform, and two did not interact for longer than 15 seconds. These 10 patients are excluded from the PP analysis.

Clinical outcomes

Complications for all patients are presented in Table 2. There were 174 complications identified in 333 patients.

In the ITT propensity-matched data, there were a total of 78 complications in 204 patients. The most common complications were ileus, wound infection, and anastomotic leak (AL), comprising 44% of complications. The most common medical complications were renal and pulmonary, comprising 14% of complications. More than one complication was recorded in 16 patients. At least one complication was identified in 52 (25%) patients. No mortality was recorded. Patients with a complication were more likely to be male (P=0.02). Age and ASA were not associated with differences in complications.

Comparisons of patients in the eHealth intervention group and standard care group with and without propensity-matched analysis is summarised in Table 3.

In the ITT propensity-matched analysis, 20 (20%) eHealth-using patients had a complication, compared to 32 (31%) non-eHealth-using patients [odds ratio (OR) =0.53; 95% confidence interval (CI): 0.26, 1.06; P=0.054] and the total number of complications was greater in the non-eHealth group (27 vs. 51 complications; OR =0.60; 95% CI: 0.30, 1.22; P=0.03). AL was present in 1% of eHealth patients and 8.8% of matched standard care patients. eHealth-using patients had significantly shorter lengths of stay in hospital, with a median of 5 days (IQR, 3–7 days) compared to 6 days (IQR, 4–11 days) for non-eHealth-using patients (P=0.02). This difference in LOS was contributed to by patients with and without complications. Although the readmission rate was almost half in the eHealth patients [6% vs. 11%; relative risk (RR) =0.55; 95% CI: 0.15, 1.61], there was no significant difference (P=0.31). The results for the PP analysis, removing the 10 patients who did not have a meaningful interaction with the eHealth intervention, were similar to the ITT analysis. The number of patients with a complication was also significantly different between groups (20% vs. 33%; OR =0.41; 95% CI: 0.21, 0.81; P=0.04).

For patients using the eHealth intervention, those with and without complications spent a median of 104 (IQR, 38–233) and 152 (IQR, 51–429) minutes on the GWH platform, respectively, P=0.52.

Discussion

This study shows that providing information on a digital platform that enhances an already established ERAS programme was well-liked by patients and was associated with a shorter LOS and fewer complications.

The reduction in LOS by a median of 1 day is consistent with the review and meta-analysis of eHealth interventions in surgery by Grygorian et al. This included 11 RCTs and 8 non-randomised cohort studies (15) and demonstrated a reduction in LOS in five of nine studies. As most of these studies focused on better communication and care after discharge from the hospital, it was proposed that this result may have been associated with clinicians being prepared to discharge patients earlier because of better monitoring in the community. Eustache et al., in their eHealth study in patients undergoing abdominal surgery reported a mean reduction in LOS of 1.6 days (21), which is similar to our study. Parallel use of the GWH platform we used in this study for orthopaedic surgery has also been associated with a 30% reduction in LOS in patients undergoing total hip joint replacement who were enrolled into a perioperative digital joint school (22). In our study, the reduction in stay was shared across patients with and without complications, suggesting that the eHealth content helped to optimise post-operative recovery in all patients, possibly by better preparing patients for surgery as well as improving compliance with ERAS pathways and supporting postoperative recovery.

In terms of post-discharge care, Grygorian’s review demonstrated a reduction in readmissions to the hospital [RR =0.68, number needed to treat (NNT) =37]. This is similar to our RR of 0.55. However, our rate of readmissions was relatively low, at 6% and 11%, resulting in our sample size being underpowered to assess this clinical endpoint. Other post-discharge eHealth studies have identified fewer visits to the emergency department (RR =0.78) (15,21) and a faster return to normal activities (14,23) in the eHealth group. These outcomes were not assessed in this study.

In contrast to LOS, most eHealth studies have not demonstrated a reduction in complications. The review by Grygorian et al. showed no difference in complications, RR =1.05 (95% CI: 0.77–1.43) (15). The study by Eustache et al. (21), an eHealth intervention that provided electronic delivery of standard preoperative information, as well as regular post-discharge questionnaires and support, reported a non-significant reduction in complications from 27% to 22%. One likely reason for the reduction in complications in our eHealth intervention was the provision of preoperative content with a focus on prehabilitation, which supplemented evidence-based ERAS pathways. This is in contrast to the majority of reported eHealth interventions, which have focused on improving perioperative and post-discharge care, rather than optimising care over the month before surgery. For example, many telemedicine studies focus on providing tailored recovery advice and an e-consult function, and in Grygorian’s review, only 2 of 19 interventions placed a similar emphasis on both preoperative and postoperative care. Features of our eHealth intervention that may have contributed to a reduction in complications included: (I) covering all of the patients’ journey, from diagnosis (approximately a month before surgery) to up to a month after discharge from the hospital. (II) A modular design where concise summarised information is delivered, with an emphasis on practical advice. (III) Provision of the right advice at the right time based around a timetable of key events. (IV) The ability to invite a friend or family member onto the platform. (V) An eConsult function. (VI) Information about the surgery, including realistic discussion of the surgical and anaesthetic risk. (VII) Explanation of the principles and details of our ERAS program. (VIII) Detailed and practical information about how the patient can lower his or her surgical risk. This included advice regarding diet, exercise, smoking, alcohol, anaemia, and information on diabetes control for patients with diabetes. (IX) Access to online exercise programs and preoperative illustration of exercises that will help with postoperative breathing and mobility. (X) Creating a supportive and positive environment where patients felt they were empowered to address health problems. (XI) In hospital daily ERAS targets. (XII) Explanation of postoperative problems and advice on how to respond. (XIII) Regular postoperative monitoring to check on progress. We propose that this comprehensive package of care is the most likely explanation for the reduction of complications seen in patients using our eHealth intervention.

Adherence to the eHealth platform of 90% of those enrolled in the study was similar to other studies that found 80–90% adherence. As varying definitions are used to define adherence (14,22,24), these adherence figures are best interpreted as being consistent with regular use of the eHealth intervention. Our eHealth platform was well used by patients, with more than 50% of patients recording greater than 2 hours of use. This was consistent with the patient ratings, where over 70% of content was rated as “really helpful”. These results are similar to other eHealth interventions (21,24,25), who report that once patients are engaged, they find the information received helpful and that this results in higher overall satisfaction with care. An additional observation in our study is that these benefits also apply to elderly patients with medical co-morbidities. Approximately one-third had significant co-morbidity, as indicated by ASA, and the majority of patients in this cohort were in their seventh decade. In contrast, in other studies, most patients were in their fifth or sixth decade (14,21,25). Additionally, 50% of these patients underwent higher-risk left-sided colorectal resections. The mix of age, comorbidity, and surgical complexity, which is representative of colorectal surgical patients at Dunedin Hospital, combined with the high patient ratings, supports the feasibility and widespread use of eHealth interventions for a broad range of surgical patients, including those in older age groups undergoing major surgery.

Limitations

This study has a number of limitations. Only one-third of patients used the eHealth platform during the time it was available. There were two issues with recruitment. The CNS staff promoting the eHealth intervention had other important clinical roles, resulting in patients not being offered the intervention when there were staffing shortages. This accounted for the majority of patients not using the eHealth intervention. Importantly, some patients declined to use the intervention, and unfortunately, the reasons for this were not documented. We did perform a qualitative study when we introduced the eHealth intervention, which included patients who thought they would struggle with using the GWH platform. On seven of eight occasions, they found that the platform was easier to use than they expected or that the use of a support person with some computer literacy helped. Low uptake of eHealth interventions has been documented in other studies where patient uptake ranged from 30% to 50% (14,21,22,26). We believe making our eHealth intervention the default standard of care at Dunedin Hospital, with appropriate integration into other hospital and clinical pathways, would dramatically improve recruitment. Once initial barriers are overcome, the adherence and ratings suggest that the majority of patients are likely to appreciate the content provided (23). Secondly, this study is a cohort study rather than an RCT. The retrospective nature of our data and the structure of our clinical records did not allow for systematic collection or extraction of adherence information for each ERAS protocol element at the individual patient level. This is a recognised challenge in retrospective ERAS research, as even studies specifically designed to prospectively measure adherence demonstrate heterogeneity in how compliance and adherence are defined and reported, with only 20% of ERAS studies providing comprehensive adherence data (27). While our institution has previously demonstrated high adherence rates with key ERAS components, we acknowledge that differential adherence between groups cannot be excluded and may introduce potential bias. Future prospective studies that incorporate routine ERAS adherence monitoring will be important to understand the independent effects of eHealth interventions and protocol compliance on surgical outcomes.

To minimise confounding, we performed propensity score matching for operative procedure, age, and ASA score, achieving a good-quality match for the primary variables measured. However, while propensity matching balances observed covariates, it cannot eliminate confounding from unmeasured or unknown factors. Characteristics such as patient motivation, socioeconomic status, baseline health literacy, or nuances in perioperative care delivery may not have been fully captured in available datasets and could still influence clinical outcomes.

Lastly, there was a difference in the AL rates between groups, with AL being less common in the eHealth intervention group. This finding was unexpected. Reducing the frequency of AL is an important objective in colorectal surgery. While the eHealth intervention may have contributed to improvements in preparation for surgery, such as a reduction in smoking and improvement in preoperative exercise, the difference in AL may also be explained by unidentified confounding factors. Additional work is required to further explore this finding.

Conclusions

In conclusion, the use of an eHealth platform with prehabilitation and rehabilitation content may reduce complications and length of stay for colorectal surgical patients on an ERAS pathway. The eHealth platform provided helpful information, improved patient engagement with their care, and as well as improving clinical outcomes, is likely to reduce hospital costs. A well-designed randomised controlled trial would provide stronger evidence for this eHealth intervention.

Acknowledgments

The authors acknowledge the assistance of the Otago Clinical Audit team in retrieving the data used in this analysis.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-41/rc

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-41/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-41/prf

Funding: This work was supported by the Department of Surgery and Critical Care, Dunedin School of Medicine, University of Otago.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-41/coif). S.B. reports that she is the co-owner of SHI Global Limited which owns Go Well Health, the eHealth platform used in this study to provide data for this study and that the license for the eHealth platform from which the data was extracted was paid for by Health New Zealand Te Whatu Ora Southern for use at Dunedin Hospital. C.M. reports that he received a scholarship from the University of Otago for his academic course-work, which included this project. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The University of Otago (No. H24/0070) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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