Elective Surgical Procedures
Conditions
Brief summary
Many people have inpatient surgery each day. Most people will have no complications but some will have minor or serious complications. The risk of having complications can vary from one person to another depending on personal factors such as; age, medical conditions such as diabetes and whether someone smokes or takes certain medications. The Investigators have learned that people want more information about their surgeries, both the general information about the risk for complications, but also more specific information about whether they are personally more or less likely to have complications. Patients are also interested in practical information such as how long they might stay in the hospital and what the recovery period will be like for them. Receiving more information can help decrease a person's level of anxiety about their surgery. The Investigators are doing this study with the assistance of the mHealth Lab at The Ottawa Hospital (a team that develops simple technologies for managing health information). The Investigators will implement and evaluate a novel, innovative tablet-based, patient-oriented risk communication application to evaluate patient knowledge of their own surgical risk before and after their visit to the Pre-Admission Unit (PAU). The Investigators will also be exploring any potential levels of anxiety before and after the PAU visit, in addition to patient satisfaction with their PAU visit. The Investigators hypothesize that it will: improve patient knowledge and experience, not increase anxiety, be acceptable to patients and clinicians, and will improve care efficiency for TOH surgical patients.
Detailed description
One and a half million Canadians have inpatient surgery every year, and many experience serious complications. Research shows that patient-specific risks are not routinely or effectively communicated to patients before surgery, despite the requirement for such information to be included as part of the informed consent process, and clear recommendations from best practice guidelines. Mobile health technology can address this gap by engaging and empowering patients to provide their own health data to generate personalized risk estimates. Using a tablet-based platform, these risk estimates can then be communicated to patients in a format that is appealing and understandable. However, such an application and process do not exist. The Investigators will address this knowledge gap through the development, implementation, and evaluation of a novel patient-oriented personalized preoperative risk communication eHealth application to empower patients, support shared decision making, and improve patient-centered outcomes. Development of this eHealth application will lead to an inclusively designed product tailored to the technology needs of elective surgery patients, who are typically older, and have limited technological expertise and comfort. The application will also be useful, in that it will communicate personalized risk estimates in a format consistent with best practices for risk communication to patients, and provide tools to engage shared discussions between patients and clinicians. Through implementation of the eHealth app, the Investigators will evaluate the effectiveness and value of personalized preoperative risk communication in improving knowledge, and satisfaction. The Investigators will also measure the acceptability of this process to patients and clinicians. Finally, the Investigators will test the feasibility of having a patient-oriented personalized risk communication application connect to the perioperative health system to identify high resource use patients prior to hospital admission.
Interventions
OTHERPatient-oriented, personalized risk communication eHealth application
The application features simple and consistent design, large well-lit buttons that tolerate tremor, and simple language. The process will be applied in preoperative clinics at The Ottawa Hospital (TOH), where patients will be asked to provide their personal health history through a series of questions already used on our TOH preoperative health screener. These values will then populate the NSQIP Universal Risk Calculator, which we have calibrated to TOH data, to generate personalized risks of mortality, serious complications, and hospital length of stay. These risk estimates will be communicated directly to the patient using absolute risk estimates represented pictorially (best practice for risk communication to patients). Risk estimates will also be provided to the patient's clinician.
Sponsors
Ottawa Hospital Research Institute
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* English or French speaking * Major elective, non-cardiac inpatient surgery
Exclusion criteria
* Unable to communicate in English or French * Unable to consent without a Substitute Decision Maker * Scheduled for non-elective surgery * Patients having same-day surgery (outpatient surgery)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Patient Knowledge | 1 day | Change in patient knowledge of their risk profile from before their Preadmission Unit (PAU). The questionnaire will not test perceived knowledge, but will test factual items specifically related to the patient's personalized risk profile.The pre- and post-appointment knowledge scores will be normalized on a 100 point scale \[(questions correct/total questions)\*100\], scale scores will range from 0 to 100 (0=all questions incorrect, 100=all questions correct). A question was marked as correct if the patient's self-estimated risk and their model estimated risk were within the same range categoryHigher scores indicate higher levels of knowledge. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Patient Satisfaction- Likelihood to Recommend | 1 day | Patient experience will be assessed using a likelihood to recommend measurement based on a 10-point Likert scale (0 being not at all likely and 10 being extremely likely) to recommend the approach used to tell you about your surgical risk to a friend or family member) as recommended by the Institute for Healthcare Improvement's Triple Aim measurement guide.The value of the intervention will be defined using a patient-perceived equation as proposed by Morgan et al. |
| Patient Acceptability- Ease of Use | 1 day | Patient acceptability of the application will be assessed using a 5-point likert scale based on how easy it was to use (1 being the easiest and 5 being the hardest). The number of participants who find the application easy to use or very easy to use (gave a 1 or 2 on the likert scale) will be reported. |
| Clinician Change in Management | 1 day | Likelihood to change management will be measured using a 5-point scale, (1 strongly agree and 5 being strongly disagree) to change the management of the patient after reviewing the information provided by the personalized risk calculator. The number of participants that indicated that they strongly or moderately agreed (gave a score of 1 or 2 on the likert scale) that the application would lead to them making a change in anesthetic management will be reported. |
| Anxiety | 1 day | Measured using the Short Form State-Trait Anxiety Inventory: at baseline and after the consultation using the short form State-Trait Anxiety Inventory, which is normalized to a 100-point scale. Scores can range from 20 to 80. Higher scores indicate greater levels of anxiety. Specifically, scores below 40 suggest minimal anxiety, while scores equal to or greater than 40 are considered clinically significant, indicating that the individual may be experiencing a heightened level of anxiety that could require further attention. |
| Proportion of Patients for Whom a Risk Score Can be Calculated | Through study completion for all participants, an average of one year | The proportion of patients for whom a risk score could be calculated - the number of participants who were able to have a risk score calculated out of the total sample will be reported (the number of participants who were able to provide the data required to calculate their personalized risk scores successfully using the application out of the total number of participants). |
| Proportion of Missing Data From the Patient-centered Health Questionnaire | Through study completion for all participants, an average of one year | Proportion of missing data from the patient-centered health questionnaire - the number of participants with missing data for the patient-centered health questionnaire that is used to calculate risk out of the total sample will be reported. |
| Clinician Acceptability | 1 day | Clinician acceptability of the application will be assessed using a 5-point likert scale (strongly disagree, moderately disagree, moderately agree, strongly agree or no opinion). The number of participants that strongly or moderately agreed that the personalized risk profiles were clear and unambiguous will be reported. |
Countries
Canada
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| Pre-Implementation Phase Participants in this group (before the eHealth app is implemented in the PAU) will be English or French speaking patients, aged 18 and older, scheduled for major non-cardiac elective surgery.
Patients will be recruited using standardized procedures, and process and outcome measures will be recorded using the same tools and methods in both study phases to decrease the risk of measurement and selection bias. | 90 |
| Post-Implementation Phase Participants in this group (after the eHealth app is implemented in the PAU) will be English or French speaking patients, aged 18 and older, scheduled for major non-cardiac elective surgery.
Patient-oriented, personalized risk communication eHealth application: The application features simple and consistent design, large well-lit buttons that tolerate tremor, and simple language. The process will be applied in preoperative clinics at The Ottawa Hospital (TOH), where patients will be asked to provide their personal health history through a series of questions already used on our TOH preoperative health screener. These values will then populate the NSQIP Universal Risk Calculator, which we have calibrated to TOH data, to generate personalized risks of mortality, serious complications, and hospital length of stay. These risk estimates will be communicated directly to the patient using absolute risk estimates represented pictorially (best practice for risk communication to patients). Risk estimates will also be provided to the patient's clinician. | 93 |
| Total | 183 |
Withdrawals & dropouts
| Period | Reason | FG000 | FG001 |
|---|---|---|---|
| Overall Study | Ineligible procedure, data error, patient declined because of timing, MD not consented | 14 | 4 |
Baseline characteristics
| Characteristic | Pre-Implementation Phase | Post-Implementation Phase | Total |
|---|---|---|---|
| Age, Continuous | 57 years STANDARD_DEVIATION 14 | 64 years STANDARD_DEVIATION 11 | 61 years STANDARD_DEVIATION 13 |
| BMI | 30 kg/m2 STANDARD_DEVIATION 8 | 30 kg/m2 STANDARD_DEVIATION 6 | 30 kg/m2 STANDARD_DEVIATION 7 |
| Race and Ethnicity Not Collected | — | — | 0 Participants |
| Sex: Female, Male Female | 48 Participants | 34 Participants | 82 Participants |
| Sex: Female, Male Male | 42 Participants | 59 Participants | 101 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 0 | 0 / 0 |
| other Total, other adverse events | 0 / 0 | 0 / 0 |
| serious Total, serious adverse events | 0 / 0 | 0 / 0 |
Outcome results
Primary
Patient Knowledge
Change in patient knowledge of their risk profile from before their Preadmission Unit (PAU). The questionnaire will not test perceived knowledge, but will test factual items specifically related to the patient's personalized risk profile.The pre- and post-appointment knowledge scores will be normalized on a 100 point scale \[(questions correct/total questions)\*100\], scale scores will range from 0 to 100 (0=all questions incorrect, 100=all questions correct). A question was marked as correct if the patient's self-estimated risk and their model estimated risk were within the same range categoryHigher scores indicate higher levels of knowledge.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Pre-Implementation Phase | Patient Knowledge | 32.2 score on a scale | Standard Deviation 28.5 |
| Post-Implementation Phase | Patient Knowledge | 53 score on a scale | Standard Deviation 33.1 |
Secondary
Anxiety
Measured using the Short Form State-Trait Anxiety Inventory: at baseline and after the consultation using the short form State-Trait Anxiety Inventory, which is normalized to a 100-point scale. Scores can range from 20 to 80. Higher scores indicate greater levels of anxiety. Specifically, scores below 40 suggest minimal anxiety, while scores equal to or greater than 40 are considered clinically significant, indicating that the individual may be experiencing a heightened level of anxiety that could require further attention.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Pre-Implementation Phase | Anxiety | 36.3 score on a scale | Standard Deviation 11.6 |
| Post-Implementation Phase | Anxiety | 29.4 score on a scale | Standard Deviation 11.6 |
Secondary
Clinician Acceptability
Clinician acceptability of the application will be assessed using a 5-point likert scale (strongly disagree, moderately disagree, moderately agree, strongly agree or no opinion). The number of participants that strongly or moderately agreed that the personalized risk profiles were clear and unambiguous will be reported.
Time frame: 1 day
Population: The app was not used in the pre-implementation phase, therefore we did not obtain feedback from clinicians in the first phase.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Post-Implementation Phase | Clinician Acceptability | 51 Participants |
Secondary
Clinician Change in Management
Likelihood to change management will be measured using a 5-point scale, (1 strongly agree and 5 being strongly disagree) to change the management of the patient after reviewing the information provided by the personalized risk calculator. The number of participants that indicated that they strongly or moderately agreed (gave a score of 1 or 2 on the likert scale) that the application would lead to them making a change in anesthetic management will be reported.
Time frame: 1 day
Population: The app was not used in the pre-implementation phase, therefore we did not obtain feedback from clinicians in the first phase.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Pre-Implementation Phase | Clinician Change in Management | 0 Participants |
| Post-Implementation Phase | Clinician Change in Management | 2 Participants |
Secondary
Patient Acceptability- Ease of Use
Patient acceptability of the application will be assessed using a 5-point likert scale based on how easy it was to use (1 being the easiest and 5 being the hardest). The number of participants who find the application easy to use or very easy to use (gave a 1 or 2 on the likert scale) will be reported.
Time frame: 1 day
Population: Participant feedback was not obtained for the pre-implementation phase as the app was not used.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Pre-Implementation Phase | Patient Acceptability- Ease of Use | 0 Participants |
| Post-Implementation Phase | Patient Acceptability- Ease of Use | 87 Participants |
Secondary
Patient Satisfaction- Likelihood to Recommend
Patient experience will be assessed using a likelihood to recommend measurement based on a 10-point Likert scale (0 being not at all likely and 10 being extremely likely) to recommend the approach used to tell you about your surgical risk to a friend or family member) as recommended by the Institute for Healthcare Improvement's Triple Aim measurement guide.The value of the intervention will be defined using a patient-perceived equation as proposed by Morgan et al.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Pre-Implementation Phase | Patient Satisfaction- Likelihood to Recommend | 7.9 score on a scale | Standard Deviation 2.5 |
| Post-Implementation Phase | Patient Satisfaction- Likelihood to Recommend | 8.9 score on a scale | Standard Deviation 2.2 |
Secondary
Proportion of Missing Data From the Patient-centered Health Questionnaire
Proportion of missing data from the patient-centered health questionnaire - the number of participants with missing data for the patient-centered health questionnaire that is used to calculate risk out of the total sample will be reported.
Time frame: Through study completion for all participants, an average of one year
Population: The app was not used in the pre-implementation phase, therefore this is not applicable to the first phase.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Pre-Implementation Phase | Proportion of Missing Data From the Patient-centered Health Questionnaire | 0 Participants |
| Post-Implementation Phase | Proportion of Missing Data From the Patient-centered Health Questionnaire | 0 Participants |
Secondary
Proportion of Patients for Whom a Risk Score Can be Calculated
The proportion of patients for whom a risk score could be calculated - the number of participants who were able to have a risk score calculated out of the total sample will be reported (the number of participants who were able to provide the data required to calculate their personalized risk scores successfully using the application out of the total number of participants).
Time frame: Through study completion for all participants, an average of one year
Population: The app was not used in the pre-implementation phase, therefore this is not applicable to the first phase.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Pre-Implementation Phase | Proportion of Patients for Whom a Risk Score Can be Calculated | 0 Participants |
| Post-Implementation Phase | Proportion of Patients for Whom a Risk Score Can be Calculated | 93 Participants |