Applying the Synergy Model to inform the nursing model of care in an inpatient and an ambulatory care setting: The experience of two urban cancer institutions, Hamilton Health Sciences and Grand River Regional Cancer Centre.

The incidence and prevalence of cancer continues to rise throughout Canada. Approximately one in two Canadians are expected to develop cancer at some point in their lives (Canadian Cancer Society, 2021). As the complexity and acuity of individuals with cancer increases, there is increased necessity to define the ideal nurse-to-patient ratio and patient caseload for nurses in specialized oncology settings. Two senior nurse leaders, faced with the need to determine the most appropriate model to inform the nursing model of care within their respective care areas, collaborated and decided to implement the Synergy Model. The Synergy Model is a professional practice model developed by the American Association of Critical Care Nurses (AACN). In the Synergy Model, nursing care reflects the integration of nurses' knowledge, skills, attitudes, competencies, and experience to meet the needs of patients and families (Curley, 2007). This model provides a framework for matching nursing resources based on patient care needs and has been adapted in various care settings. The model, however, has not been applied in a surgical oncology inpatient unit or in an oncology ambulatory care setting. Using a quality improvement methodology, the Synergy Model was piloted in these new areas and found to be effective. The Synergy Model can be utilized to determine the need for additional nursing resources with specialized oncology nurses and appropriate skill mix of intraprofessional nursing teams. It can also be used to assess adult oncology patients who present to the ambulatory systemic care suite for unscheduled care related to symptomatic concerns.

Risk Factors for Hospital Readmission Following Noncardiac Surgery: International Cohort Study.

Objective: To determine timing and risk factors associated with readmission within 30 days of discharge following noncardiac surgery. Background: Hospital readmission after noncardiac surgery is costly. Data on the drivers of readmission have largely been derived from single-center studies focused on a single surgical procedure with uncertainty regarding generalizability. Methods: We undertook an international (28 centers, 14 countries) prospective cohort study of a representative sample of adults ≥45 years of age who underwent noncardiac surgery. Risk factors for readmission were assessed using Cox regression (ClinicalTrials.gov, NCT00512109). Results: Of 36,657 eligible participants, 2744 (7.5%; 95% confidence interval [CI], 7.2-7.8) were readmitted within 30 days of discharge. Rates of readmission were highest in the first 7 days after discharge and declined over the follow-up period. Multivariable analyses demonstrated that 9 baseline characteristics (eg, cancer treatment in past 6 months; adjusted hazard ratio [HR], 1.44; 95% CI, 1.30-1.59), 5 baseline laboratory and physical measures (eg, estimated glomerular filtration rate or on dialysis; HR, 1.47; 95% CI, 1.24-1.75), 7 surgery types (eg, general surgery; HR, 1.86; 95% CI, 1.61-2.16), 5 index hospitalization events (eg, stroke; HR, 2.21; 95% CI, 1.24-3.94), and 3 other factors (eg, discharge to nursing home; HR, 1.61; 95% CI, 1.33-1.95) were associated with readmission. Conclusions: Readmission following noncardiac surgery is common (1 in 13 patients). We identified perioperative risk factors associated with 30-day readmission that can help frontline clinicians identify which patients are at the highest risk of readmission and target them for preventive measures.

Implementing the Synergy Model: A Qualitative Descriptive Study.

Hospitals across our nation are seeking to implement models of care that meet the primary goals of Quadruple Aim: Improved population health, cost-effective care delivery, and patient and provider satisfaction. In an effort to address the Quadruple Aim and our patients' care needs, Hamilton Health Sciences (HHS) embarked on a model of care delivery redesign, beginning with nursing care delivery. From 2013 to 2018, 12 clinical programs at HHS implemented the Synergy Model with its accompanying synergy patient needs assessment tool for nurses to objectively assess patients' acuity and dependency needs. Data on patients' priority care needs were used to inform a nursing model of care redesign at HHS, including skill mix and staffing levels. This five-year project was an organization-wide quality improvement initiative. As part of the evaluation, HHS leaders partnered with health services nurse researchers to conduct a mixed methods study. This paper describes the evaluation outcomes from the qualitative component of the study, which included interviews with clinical nurse leaders and direct care nurses. Data were analyzed using descriptive thematic analysis. Some key findings were increased nurse awareness of patients' holistic care needs and leaders' capacity to plan staffing assignments based on patients' priority care needs. Themes helped inform recommendations for key stakeholders, including nurse leaders and direct care nurses.

Beyond Wellness Monitoring: Continuous Multiparameter Remote Automated Monitoring of Patients.

The pursuit of more efficient patient-friendly health systems and reductions in tertiary health services use has seen enormous growth in the application and study of remote patient monitoring systems for cardiovascular patient care. While there are many consumer-grade products available to monitor patient wellness, the regulation of these technologies varies considerably, with most products having little to no evaluation data. As the science and practice of virtual care continues to evolve, clinicians and researchers can benefit from an understanding of more comprehensive solutions capable of monitoring multiple biophysical parameters (eg, oxygen saturation, heart rate) continuously and simultaneously. These devices, herein referred to as continuous multiparameter remote automated monitoring (CM-RAM) devices, have the potential to revolutionise virtual patient care. Through seamless integration of multiple biophysical signals, CM-RAM technologies can allow for the acquisition of high-volume big data for the development of algorithms to facilitate early detection of negative changes in patient health status and timely clinician response. In this article, we review key principles, architecture, and components of CM-RAM technologies. Work to date in this field and related implications are also presented, including strategic priorities for advancing the science and practice of CM-RAM.

A Case Study of Two Emerging Nurse Leaders Specializing in Virtual Health during the COVID-19 Global Pandemic.

In the field of digital health research, nurse leaders have an opportunity to be integral to the design, implementation and evaluation of virtual care interventions. This case study details the experiences of two emerging nurse leaders during the COVID-19 pandemic in providing research and clinical leadership for a national virtual health trial. These nurse leaders trained and led a national team of 70 nurses across eight participating centres delivering the virtual care and remote monitoring intervention, using the normalization process theory. This case study presents a theoretically informed approach to training and leadership and discusses the experiences and lessons learned.

Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM): protocol for a randomized controlled trial.

BACKGROUND: After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. METHODS: We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. INTERPRETATION: This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT04344665.

Post-discharge after surgery Virtual Care with Remote Automated Monitoring-1 (PVC-RAM-1) technology versus standard care: randomised controlled trial.

OBJECTIVE: To determine if virtual care with remote automated monitoring (RAM) technology versus standard care increases days alive at home among adults discharged after non-elective surgery during the covid-19 pandemic. DESIGN: Multicentre randomised controlled trial. SETTING: 8 acute care hospitals in Canada. PARTICIPANTS: 905 adults (≥40 years) who resided in areas with mobile phone coverage and were to be discharged from hospital after non-elective surgery were randomised either to virtual care and RAM (n=451) or to standard care (n=454). 903 participants (99.8%) completed the 31 day follow-up. INTERVENTION: Participants in the experimental group received a tablet computer and RAM technology that measured blood pressure, heart rate, respiratory rate, oxygen saturation, temperature, and body weight. For 30 days the participants took daily biophysical measurements and photographs of their wound and interacted with nurses virtually. Participants in the standard care group received post-hospital discharge management according to the centre's usual care. Patients, healthcare providers, and data collectors were aware of patients' group allocations. Outcome adjudicators were blinded to group allocation. MAIN OUTCOME MEASURES: The primary outcome was days alive at home during 31 days of follow-up. The 12 secondary outcomes included acute hospital care, detection and correction of drug errors, and pain at 7, 15, and 30 days after randomisation. RESULTS: All 905 participants (mean age 63.1 years) were analysed in the groups to which they were randomised. Days alive at home during 31 days of follow-up were 29.7 in the virtual care group and 29.5 in the standard care group: relative risk 1.01 (95% confidence interval 0.99 to 1.02); absolute difference 0.2% (95% confidence interval -0.5% to 0.9%). 99 participants (22.0%) in the virtual care group and 124 (27.3%) in the standard care group required acute hospital care: relative risk 0.80 (0.64 to 1.01); absolute difference 5.3% (-0.3% to 10.9%). More participants in the virtual care group than standard care group had a drug error detected (134 (29.7%) v 25 (5.5%); absolute difference 24.2%, 19.5% to 28.9%) and a drug error corrected (absolute difference 24.4%, 19.9% to 28.9%). Fewer participants in the virtual care group than standard care group reported pain at 7, 15, and 30 days after randomisation: absolute differences 13.9% (7.4% to 20.4%), 11.9% (5.1% to 18.7%), and 9.6% (2.9% to 16.3%), respectively. Beneficial effects proved substantially larger in centres with a higher rate of care escalation. CONCLUSION: Virtual care with RAM shows promise in improving outcomes important to patients and to optimal health system function. TRIAL REGISTRATION: ClinicalTrials.gov NCT04344665.