▬
Below Average▬
Average▬
Above Average5.6 days
5.0 days
10.1 %
8.2 %
13.4 %
6.4 %
0.9 tests
7.6 tests
96.0 %
25.6 %
| My Hospital |
All Hospitals |
|
|---|---|---|
| Number of Unique Hospitalizations | 185 | 6,078 |
| Age, Median (25th-75th) | 73 (65-81) | 72 (66-82) |
| Female | 56 % | 52 % |
|
High Comorbidity at Admission |
36 % | 33 % |
|
Admission on Weekends |
17 % | 26 % |
|
Admission at Night |
57 % | 80 % |
|
Admission by Season |
||
| Spring | 21 % | 22 % |
| Summer | 15 % | 22 % |
| Fall | 23 % | 23 % |
| Winter | 41 % | 33 % |
|
Predicted Risk of Death, Median (25th-75th) |
0.07 (0.04-0.12) | 0.07 (0.05-0.11) |
|
Neighborhood-Level After Tax Income (000s), Median (25th-75th) |
43 (41-68) | 46 (39-71) |
|
Neighbourhood-Level Percent Visible Minority, Median (25th-75th) |
35 (31-39) % | 29 (26-35) % |
|
Discharged to Long-Term Care Home |
7 % | 9 % |
| No Health Card Number | 9 % | 11 % |
|
No Postal Code |
11 % | 13 % |
Number of days from admission to discharge, including ALC days.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at admission, modified laboratory-based acute physiology score at admission, elective admission, and number of acute care admissions in the past 6 months.
Hospitalizations that were transferred in from another acute care institution, transferred out to another acute care institution, or with total length of stay greater than 365 days.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted total length of stay at my hospital
5.6
(5.3-5.7)
Days, Median (25th-75th)
Unadjusted total length of stay for patients at all hospitals
5.7
(5.6-6.7)
Days, Median (25th-75th)
Unadjusted total length of stay at the 25th percentile hospital
4.5
(4.4-4.6)
Days, Median (25th-75th)
risk-adjusted
total length of stay compare to other hospitals?risk-adjusted
total length of stay break down by condition?
Number of days from admission to discharge, excluding ALC days.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at admission, modified laboratory-based acute physiology score at admission, elective admission, and number of acute care admissions in the past 6 months.
Hospitalizations that were transferred in from another acute care institution, transferred out to another acute care institution, or with total length of stay greater than 365 days.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted acute length of stay at my hospital
5.0
(4.4-5.4)
Days, Median (25th-75th)
Unadjusted acute length of stay for patients at all hospitals
5.1
(5.0-5.6)
Days, Median (25th-75th)
Unadjusted acute length of stay at the 25th percentile hospital
4.2
(4.0-4.3)
Days, Median (25th-75th)
risk-adjusted
acute length of stay compare to other hospitals?risk-adjusted
acute length of stay break down by condition?
ALC Days/Total Days is defined as the percentage of total inpatient days that were designated as ALC.
ALC Days/ALC Patient is defined as the median number of ALC days among patients with any designated ALC days.
None
Hospitalizations with total length of stay greater than 365 days.
Unadjusted ALC days at my hospital
10.1 %
4.2
(2.8-5.3)
ALC days / total days
ALC days / ALC patient,
Median (25th-75th)
Unadjusted ALC days for patients at all hospitals
10.5 %
3.1
(2.5-4.3)
ALC days / total days
ALC Days / ALC Patient, Median (25th-75th)
Unadjusted ALC days at the 25th percentile hospital
9.1 %
2.3
(1.3-2.9)
ALC days / total days
ALC days / ALC patient, median (25th-75th)
Episode of care. An episode of care refers to all contiguous inpatient hospitalizations to any medical or intensive care service within GeMQIN. For episodes of care involving acute care transfers, readmissions are attributed to the last hospital from which the patient was discharged before readmission.
Episodes of care that were followed by readmission to any medical or intensive care service at a GeMQIN hospital within 7 days of discharge.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at index admission, modified laboratory-based acute physiology score at index admission, elective index admission, and number of acute care hospitalizations in the past 6 months.
All hospitalizations with an invalid health card number are excluded because these encounters do not have a patient-level identifier, so readmission cannot be assigned. The following episodes of care are excluded: episodes with discharge as death, episodes with at least 1 record for palliative care or mental health as most responsible discharge diagnosis, episodes where a patient was transferred to and discharged from a hospital outside of the GeMQIN network, and episodes where the last record was self sign-out. The following are not counted as readmissions: episodes where the first record was elective, episodes with at least 1 record for: chemotherapy for neoplasm, palliative care, obstetric delivery, mental health, medical assistance in dying.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted 7-day readmission rate at my hospital
8.2 %
Rate
Unadjusted 7-day readmission rate for patients at all hospitals
9.7 %
Rate
Unadjusted 7-day readmission rate at the 25th percentile hospital
5.1 %
Rate
risk-adjusted
7-day readmission rate compare to other hospitals?risk-adjusted
7-day readmission rate break down by condition?
Episode of care. An episode of care refers to all contiguous inpatient hospitalizations to any medical or intensive care service within GeMQIN. For episodes of care involving acute care transfers, readmissions are attributed to the last hospital from which the patient was discharged before readmission.
Episodes of care that were followed by readmission to any medical or intensive care service at a GeMQIN hospital within 30 days of discharge.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at index admission, modified laboratory-based acute physiology score at index admission, elective index admission, and number of acute care hospitalizations in the past 6 months.
All hospitalizations with an invalid health card number are excluded because these encounters do not have a patient-level identifier, so readmission cannot be assigned. The following episodes of care are excluded: episodes with discharge as death, episodes with at least 1 record for palliative care or mental health as most responsible discharge diagnosis, episodes where a patient was transferred to and discharged from a hospital outside of the GeMQIN Network, and episodes where the last record was self sign-out. The following are not counted as readmissions: episodes where the first record was elective, episodes with at least 1 record for: chemotherapy for neoplasm, palliative care, obstetric delivery, mental health, medical assistance in dying.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted 30-day readmission rate at my hospital
13.4 %
Rate
Unadjusted 30-day readmission rate for patients at all hospitals
14.8 %
Rate
Unadjusted 30-day readmission rate at the 25th percentile hospital
9.9 %
Rate
risk-adjusted
30-day readmission rate compare to other hospitals?risk-adjusted
30-day readmission rate break down by condition?
Death occurring within hospital.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at admission, modified laboratory-based acute physiology score at admission, elective admission, total length of stay, transfer in from another acute care institution, and COVID-19 as a risk factor.
Consistent with CIHI, risk-adjusted assessments of hospital performance are limited to diagnosis groups accounting for 80% of in-hospital deaths in GeMQIN participating hospitals. The default setting of this indicator page is to exclude hospitalizations with palliative care as the most responsible discharge diagnosis when determining these diagnosis groups. When the “Exclude” filter on the top left of the indicator page is set to “Nothing”, hospitalizations with palliative care as the most responsible discharge diagnosis are included.
Hospitalizations of patients older than 120 years of age or with total length of stay greater than 365 days. The “Exclude” tab for this indicator allows you to exclude hospitalizations for palliative care and medical assistance in dying (MAID) throughout the indicator page. Please note that palliative care coding practices are inconsistent across hospitals.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted mortality at my hospital
All deaths (% discharges)
MAID (% of deaths)
Palliative (% of deaths)
Unadjusted mortality for patients at all hospitals
All deaths (% discharges)
MAID (% of deaths)
Palliative (% of deaths)
Unadjusted mortality at the 25th percentile hospital
All deaths (% of discharges)
risk-adjusted
mortality compare to other hospitals?risk-adjusted
mortality break down by condition?
The total number of advanced imaging tests per hospitalization. Advanced imaging tests are defined as computed tomography, ultrasound, and magnetic resonance imaging. Interventional radiology is not included. Results for each individual imaging modality can be viewed using the filter on the top-left of the page.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at admission, modified laboratory-based acute physiology score at admission, elective admission, and number of acute care admissions in the past 6 months. Risk adjustment is specific to each imaging modality.
Hospitalizations with total length of stay greater than 365 days.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted number of advanced imaging tests at my hospital
Tests per hospitalization
Unadjusted number of advanced imaging tests for patients at all hospitals
Tests per hospitalization
Unadjusted number of advanced imaging tests at the 25th percentile hospital
Tests per hospitalization
risk-adjusted
number of advanced imaging tests compare to other hospitals?risk-adjusted
advanced imaging tests break down by condition?
The total number of routine bloodwork tests per hospitalization. Routine bloodwork tests are defined as electrolyte tests and complete blood count tests.
Assessments of hospital performance for this indicator are risk-adjusted. Risk adjustment includes patient age, sex, Charlson comorbidity index score at admission, modified laboratory-based acute physiology score at admission, elective admission, and number of acute care admissions in the past 6 months.
Hospitalizations with total length of stay greater than 365 days.
Patients with a most responsible discharge diagnosis of COVID-19 are excluded from all risk-adjusted analyses. See FAQ section “How are patients with COVID-19 handled in this report?”.
Unadjusted number of routine bloodwork tests at my hospital
7.6
Tests per hospitalization
Unadjusted routine bloodwork tests for patients at all hospitals
8.7
Tests per hospitalization
Unadjusted routine bloodwork tests at the 25th percentile hospital
6.6
Tests per hospitalization
risk-adjusted
number of routine bloodwork tests compare to other hospitals?risk-adjusted
routine bloodwork tests break down by condition?
The percentage of all red blood cell transfusions that were deemed appropriate. Appropriate red blood cell transfusions are defined by the most recent pre-transfusion hemoglobin value less than 80g/L within 48 hours prior to transfusion. Above average is desirable.
None
Red blood cell transfusions with no hemoglobin measurement within 48 hours prior to the transfusion are excluded. This is rare, occurring in approximately 2% of blood transfusions.
Unadjusted appropriate RBC transfusions at my hospital
96.0 %
Appropriate RBC transfusions / total transfusions
Unadjusted appropriate RBC transfusions for patients at all hospitals
94.9 %
Appropriate RBC transfusions / total transfusions
Unadjusted appropriate RBC transfusions at the 25th percentile hospital
97.2 %
Appropriate RBC transfusions / total transfusions
The percentage of hospitalizations that received at least 1 sedative-hypnotic order. Ideally, we would exclude patients who were prescribed sedative-hypnotic medications prior to hospital admission from this indicator. However, GEMINI does not currently hold data about pre-hospital medications. Therefore, we report a ‘Main Indicator’ and a ‘Secondary Indicator’ to address this. Results for each indicator can be viewed using the filter on the top-left of the page.
Main Indicator: We exclude patients who had a sedative-hypnotic order placed in the first 24 hours of hospital admission. This is an imperfect proxy to exclude patients who were taking sedative-hypnotic medications before hospital admission (because they would be continued at the time of admission). The indicator only includes patients whose first sedative-hypnotic prescription occurred more than 24 hours after admission.
Secondary Indicator: Because orders placed in the first 24 hours do not always reflect pre-hospital medications (for example, a new order could be part of an admission order set), we also report ‘all orders’ as a secondary indicator. In this indicator, all hospitalizations are included even if a sedative-hypnotic was ordered in the first 24 hours.
Sedative-hypnotic orders placed in the ICU are not included in the numerator. Step-down units are not considered ICU because patients requiring sedation are typically not admitted to step-down units.
PRN (‘as needed’) medication orders are included in the numerator and are treated like standing scheduled orders for all calculations and exclusion criteria. The report also provides the percentage of hospital admissions that only received PRN orders, called “PRN Orders Only”, which is a subset of all the hospital admissions that received sedative-hypnotic orders.
None
Hospitalizations with an appropriate indication for sedative-hypnotics are excluded from both indicators. These include the following: panic attacks, anxiety, seizures, catatonia, alcohol withdrawal, benzodiazepene withdrawal, neuroleptic malignant syndrome, serotonin syndrome, intoxication with cocaine or other stimulants, palliative care.
Hospitalizations with a sedative-hypnotic order at my hospital
Sedative-hypnotic orders (%)
PRN Orders Only: %
Hospitalizations with a sedative-hypnotic order for patients at all hospitals
Sedative-hypnotic orders (%)
PRN Orders Only: %
Hospitalizations with a sedative-hypnotic order at the 25th percentile hospital
Sedative-hypnotic orders (%)
(Focus on Care Transitions)
Care transitions can occur at many different times and places throughout a person’s health care journey, including during admission to hospital, referral to speciality care, discharge out of the emergency department or hospital, and admission to a long-term care facility from the person’s home. Poorly coordinated care transitions often result in poor quality of care, compromised patient safety, unfavourable experiences of care, prolonged length of stay, and unplanned readmission to hospital. Preventable causes of readmission can include:
In Ontario, hospitals continue to focus on optimizing transitions, optimizing length of stay, and reducing unexpected readmission to hospital. Readmission and length of stay are complex indicators that require a multidisciplinary team approach for large-scale, sustainable improvements of the aspects of care that they represent. The change ideas in Table 1 can help address major gaps in transition planning and care coordination.
| Change Idea | Key Action(s) |
|---|---|
|
Upon Admission to Hospital
Upon Transition Out of Hospital
|
|
Upon Admission to Hospital/During Hospital Stay
Upon Transition Out of Hospital
|
|
Upon Admission to Hospital
During Hospital Stay
Upon Transition Out of Hospital
|
|
Upon Admission to Hospital/During Hospital Stay
Upon Transition Out of Hospital
|
|
Upon Admission/During Hospital Stay/Transition Out of Hospital
|
As your division and interprofessional teams review the table above, consider the following reflective questions:
After reviewing the change ideas above, which elements are contributing to prolonged hospital length of stay? Which elements are contributing to 7-day or 30-day readmission rates at your hospital?
After reviewing hospital data, conducting audits, and other self-reflection activities, can your team identify the underlying root cause(s) for unnecessarily prolonged length of stay and unanticipated/preventable readmissions? For 7-day readmission rate, were patients ready for discharge? If not, why not? For 30-day readmission rate, were patients properly prepared to manage their care at home? How was patient risk of readmission to hospital anticipated and managed? Look for themes.
What is the relationship between shorter length of stay and 7-day readmission rate for your patients? Are there clinical conditions or discharge patterns (e.g., day of week) that have higher readmission rates?
When conducting root cause analysis, ask yourself the following questions:
a. What variations in practice can we identify (e.g., day of discharge)? What is causing variation in practice?
b. What practices or processes (or lack thereof) are increasing length of stay?
c. How is the quality of the transition contributing to need for readmission?
d. What are we doing well and how can we do this more often?
Use creative thinking techniques to identify problems and areas of focus for QI (see the box below for an example).
| Try This! |
|---|
|
Use the Theory of Inventive Reasoning (TRIZ): Use contradiction to identify opportunities for improvement by asking yourself the following questions: Design a Bad Transition:
Review Your Answers:
|
These indicators quantify how often patients in acute care settings no longer require the intensity of services or resources provided in that care setting but cannot yet be discharged and, hence, are designated ALC (i.e., they are essentially noted as having the status alternate level of care or delayed discharge) by a physician or other clinician. The period of time that this status applies encompasses the time from designation until the patient is discharged, transferred to a subsequent care destination, or their condition deteriorates such that the designation no longer applies.1
Patients and care partners may be negatively impacted when there is a high rate of ALC designation, potentially resulting in subsequent health issues (e.g., falls, functional decline, hospital-acquired infections). High rates of ALC designation may reflect hospital process issues, community capacity issues, insufficient access to long-term or postacute care, or other characteristics of a poorly functioning health care system. For example, patients may be designated ALC because the appropriate level of post-discharge care is not available in a timely manner, which extends their hospital stay. The reduction in available resources is then propagated — high rates of ALC designation can lead to cancellation of surgeries for other patients due to lack of space.2
Older adults, people with multiple comorbidities, functional impairment, or cognitive impairment, and people at a socioeconomic disadvantage or from populations that have been marginalized have higher risks of delayed discharge.3 For patients with frailty, there are risks associated with hospitalization, such as falls and delirium. Patient safety, outcomes, and health system flow are therefore all affected by high rates of ALC designation — senior-friendly approaches to care have been shown to prevent adverse events and prolonged lengths of stay in hospital.4
Many Ontario hospitals have been working to lower ALC rates. It is a persistent and complex problem that requires a multifaceted, collaborative approach. CIHI has published guidelines to support ALC designation to promote common practices by providing prompt questions for clinicians to consider for ALC designation.
See Table 1 for change ideas that can help lower ALC rates.
| Change Idea | Key Action(s) |
|---|---|
|
|
|
|
|
|
2 Kuluski K, Ho JW, Cadel L, Shearkhani S, Levy C, Marcinow M, et al. An alternate level of care plan: co-designing components of an intervention with patients, caregivers and providers to address delayed hospital discharge challenges. Health Expect. 2020;23:1155–65.
3 Bhatia D, Peckham A, Abdelhalim R, King M, Kurdina A, Ng R, et al. Alternate level of care and delayed discharge: lessons learned from abroad (Rapid Review) [Internet]. Toronto (ON): North American Observatory on Health Systems and Policies; 2020 Feb [cited 2022 Jan 04]. Available from: https://naohealthobservatory.ca/wp-content/uploads/2020/03/NAO-Rapid-Review-22_EN.pdf
4 Ontario Health. ALC avoidance leading practices guide: preventing hospitalizations and extended stays in older adults. September 2021.
This indicator quantifies how many patient deaths, defined by the Discharge Abstract Database (DAD) discharge disposition code 7, 72, 73 or 74, occur in hospital. Although some deaths are to be expected as the natural end of life stages or disease processes, some of these deaths are avoidable.1 A fundamental process in providing quality care is preventing avoidable harm or death by identifying contributing local and system-level factors. When quality issues are addressed, patients and providers are more likely to have confidence that quality care is being provided and better clinical outcomes are being achieved.2
Reviewing data and charts (that is, conducting audits) can help you understand your in-hospital mortality rate, identify underlying causes of avoidable deaths (by illuminating the scale and scope of quality issues and highlighting the errors and adverse event occurrences that may be contributing), and identify potential solutions. Chart reviews are more effective at identifying adverse events and errors than voluntary occurrence reporting.2 Without effective processes for quantifying the rate of adverse events and understanding critical details about their occurrence, teams may not focus their attention on the issues that contribute the most to avoidable suffering and death.2
This report presents your in-hospital mortality rate data alongside those of other Ontario hospitals participating in GeMQIN for comparison.
Teams are encouraged to:
See Table 1 for change ideas that can help reduce in-hospital mortality rate.
| Change Idea | Key Action(s) |
|---|---|
|
|
|
|
|
|
|
|
|
|
As your division and interprofessional teams review Table 1, reflect by considering the following questions and suggested actions:
After reviewing the change ideas, which elements has your team identified as contributing to avoidable deaths in your hospital? Which are most relevant for General Medicine teams to focus on?
After reviewing hospital data, conducting chart reviews, and other self-reflection activities, can your team identify and quantify the amount, severity, and type of adverse events that may have led to patient deaths? What quality issues have led to these adverse events?
Do these adverse events or quality issues occur more frequently during weekday, weekend, or night shifts? Can your team identify other trends or themes?
When conducting root cause analysis, ask your team the following questions:
a. What is causing these adverse events? Do any themes emerge (e.g., medication errors, infections, inability to quickly identify when a patient’s condition is deteriorating)?
b. What practices or processes contribute to these quality issues?
c. What proportion of deaths were avoidable and what were the circumstances or causes?
d. What are we doing well and how can we do this more often?
Which quality improvement initiatives will be prioritized? Do they align with other initiatives related to patient safety, transitions of care, etc.?
2 Zimmerman R, Pierson S, McLean R, McAlpine A, Caron C, Morris B, et al. Aiming for zero preventable deaths: using death review to improve care and reduce harm. Healthcare Quarterly. 2010;13(Sp):81-87. Available from: https://www.longwoods.com/content/21971/healthcare-quarterly/aiming-for-zero-preventable-deaths-using-death-review-to-improve-care-and-reduce-harm
3 Morgan DJ, Lomotan LL, Agnes K, McGrail L, Roghmann MC. Characteristics of healthcare-associated infections contributing to unexpected in-hospital deaths. Infect Control Hosp Epidemiol. 2010;31(8):864-6.
4 Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. Automated identification of adults at risk of in-hospital deterioration. N Engl J Med 2020;383:1951-60.
This indicator measures the number of advanced diagnostic imaging tests (computed tomography [CT] scans, magnetic resonance imaging [MRI], and ultrasounds) performed during inpatient admissions to hospital. This indicator can be reviewed as an aggregate or by individual modality to identify potential opportunities for improvement related to a specific modality.
While imaging is necessary to diagnose and guide treatment, it is estimated that 20% to 50% of radiologic investigations are inappropriate.1 Determining the appropriateness of advanced diagnostic imaging is complex and depends on patient characteristics, the medical condition involved, and the symptoms being investigated.
The rapid evolution of imaging technology, long wait times for certain tests, and a lack of communication among specialty clinicians, radiologists, and family physicians all exacerbate the issue of the overuse of advanced diagnostic imaging.2 Conducting diagnostic imaging tests that are unlikely to alter clinical care or patient outcomes, as well as repetitive testing and “treatment cascades,” contribute to the inappropriate use of advanced diagnostic imaging.2
Wait times for diagnostic imaging tests and image-guided procedures can lead to delayed diagnosis, adverse outcomes, and prolonged stay in hospital. In Toronto, Bartsch et al studied time to test for CT scans, MRI, ultrasounds, and peripherally inserted centralized catheters; the impact of time to test on length of stay; and other major factors contributing to delays in time to test.3 Where and when the imaging test was ordered, as well as the timing of the test order relative to admission, were found to be important contributors to delays in time to test. Notably, patients whose tests were ordered on the ward or on a weekend waited longer for testing (especially for CT scans) than patients in emergency or intensive care. Bartsch et al also found that those in the lowest-income neighborhoods, older patients, and patients with complex medical needs experienced a longer time to test; accordingly, these patients also experienced a prolonged length of stay. While not evaluated in this study, other wait times, such as time to receive diagnostic reports from radiology, time from receipt of report to review by a general internal medicine physician, and time to decision, may also affect patient care and length of stay.
| Change Idea | Key Action(s) |
|---|---|
|
|
|
|
|
|
|
|
As your division and interprofessional teams review Table 1, consider the following reflective questions and suggested actions:
If advanced diagnostic imaging procedures are overused at your facility:
a. Consult with clinicians involved in ordering tests for inpatients to understand their practice behaviours.
b. Collaborate with appropriate services (e.g., laboratory) to identify quality issues and underlying root causes and to test and implement change initiatives.
How many CT scans, MRIs, and ultrasounds were requested and performed in general medicine at your hospital over the past 12 months? For which patient conditions are most advanced diagnostic imaging tests ordered?
Are there areas where general medicine and radiology teams agree there is inappropriate use of advanced diagnostic imaging? Are there areas where they disagree? What priorities can be set jointly to reduce the inappropriate use of advanced diagnostic imaging?
Assess your hospital’s patterns with respect to time to test and time to report results in order to understand the impacts of timing (i.e., day, night, weekend) and where tests are ordered (e.g., ward, ED, ICU). What are the busiest days and times?
When conducting a root cause analysis, ask your team the following questions:
a. What practices or processes (or lack thereof) are contributing to the overuse of advanced diagnostic imaging?
b. What is working well to reduce the inappropriate use of advanced diagnostic imaging, and how can we do this more often?
c. After reviewing hospital data, conducting chart reviews, and engaging in other reflective activities, can our team identify the factors contributing to the inappropriate ordering of advanced diagnostic imaging tests? If yes, what are they?
Which change ideas are most relevant to your general medicine teams? Which are most feasible to implement?
2 Canadian Agency for Drugs and Technologies in Health. Appropriate utilization of advanced diagnostic imaging procedures: CT, MRI, and PET/CT – environmental scan [Internet]. Ottawa (ON): The Agency. 2013 Feb [cited 2022 Jan 4]. Available from: https://www.cadth.ca/media/pdf/PFDIESLiteratureScan_e_es.pdf
3 Bartsch E, Shin S, Roberts S, et al. Imaging delays among medical inpatients in Toronto, Ontario: a cohort study. PLoS One. 2023;18(2):e0281327.
This indicator measures the number of routine blood tests (i.e., electrolytes and complete blood count [CBC]) conducted during an inpatient stay.
Routine, repetitive blood work on clinically stable patients is unnecessary and may disturb sleep, reduce patient satisfaction, cause or worsen anemia, and increase the risk of adverse outcomes.1,2 It is estimated that laboratory testing influences 60% to 70% of medical decisions, which can lead to additional downstream testing and procedures.1
Studies have revealed that bundled order sets, a fear of “missing something,” and clinician habit contribute to the inappropriate ordering of blood work and other diagnostics.1 Choosing Wisely Canada, an initiative focused on reducing unnecessary treatment and tests, has prepared Pause the Draws, a toolkit to identify fundamental signals of overtesting.2
In addition, Choosing Wisely Canada invites all hospitals across Canada to join them in a concerted effort to curb low-value testing so that available lab resources can be put to better use. See whether your hospital is participating in Using Labs Wisely.
Table 1 provides change ideas to reduce the inappropriate ordering of blood work.
| Change Idea | Key Action(s) |
|---|---|
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|
|
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As your division and interprofessional teams review Table 1, consider the following reflective questions and suggested actions:
Understand ordering practices to identify the signals of overtesting2:
a. Do general medicine clinicians believe that it would be possible to decrease the number of blood tests being done without negatively affecting patient care?
b. Are blood tests ordered habitually rather than to answer a specific clinical question, even occasionally?
c. On admission, are blood tests typically ordered for a defined duration (e.g., CBC daily × 2 days), or are there open-ended standing orders for blood work?
d. How common is it for blood work to be ordered for durations longer than 3 days? How about 5 days? Is blood work ever ordered without a clear stop date?
e. Does your general medicine team use any workarounds to make ordering lab work easier?
When conducting a root cause analysis, ask yourselves the following questions:
a. What practices or processes (or lack thereof) are contributing to the inappropriate ordering of blood work?
b. What is working well to reduce the inappropriate ordering of blood work in our hospital, and how can we do this more often?
c. After reviewing hospital data, conducting chart reviews, and engaging in other reflective activities, can our team identify the factors contributing to the inappropriate ordering of blood work? If yes, what are they?
Which change ideas are most relevant to your general medicine teams? Which are most feasible to implement?
2 Choosing Wisely Canada. Pause the draws: a toolkit on reducing repetitive, “routine” blood draws in hospitals [Internet]. Toronto (ON): Choosing Wisely; 2019 [cited 2022 Jan 4]. Available from: https://choosingwiselycanada.org/wp-content/uploads/2017/10/CWC_BloodDraws_Toolkit.pdf
3 MacDonald EG, Saleh RR, Lee TC. Mindfulness-based laboratory reduction: reducing utilization through trainee-led daily “time outs.” Am J Med. 2017;130(6):e241-e244.
The appropriate blood transfusion indicator is the percentage of transfusions performed in patients with pre-transfusion hemoglobin levels of < 80 g/L. Randomized controlled trials have demonstrated that for most patients, transfusions can safely be restricted to an even lower threshold of < 70 g/L, but the higher threshold of < 80 g/L was chosen to allow individual clinical judgment in cases where levels may need to be higher due to conditions such as cardiac ischemia.1 Evidence shows that using a threshold for blood transfusions based on hemoglobin alone can still lead to inappropriate transfusions.2 Adverse events, such as infections, transfusion reactions, and increased morbidity and mortality in people receiving blood transfusions, are well documented, as is the need to reduce unnecessary blood transfusions.3
For general medicine, opportunities for improvement are informed by understanding the extent to which modifiable risk factors drive transfusion decisions. Identifying and understanding the root cause(s) of inappropriate blood transfusions are essential for informing quality improvement initiatives and improving patient care and outcomes. See Choosing Wisely Canada’s Transfusion Medicine recommendations for more information.
Table 1 provides change ideas to decrease the percentage of inappropriate blood transfusions performed. These change ideas align with best practices described in the literature.1
| Change Idea | Key Action(s) |
|---|---|
|
Key Action:
Resources:
|
|
Key Action:
Resources:
|
|
Key Action:
Resources: |
As your division and interprofessional teams review Table 1, consider the following reflective questions and suggested actions:
Which factors mentioned in Table 1 contribute to inappropriate blood transfusions in your hospital?
Do you have a plan to review hospital data, conduct audits, and engage in other self-reflection activities to identify, verify, and correct underlying reasons for inappropriate blood transfusion?
When conducting a root cause analysis, ask your team the following questions:
a. What is causing variation in blood transfusions in our hospital?
b. Which practices or processes (or lack thereof) are contributing to unnecessary blood transfusions?
c. What is working well to reduce unnecessary blood transfusions in our hospital, and how can we do this more often?
Which colleagues in other teams or divisions (e.g., laboratory medicine, hematology) can you collaborate with to reduce unnecessary blood transfusions?
Consider using this planning survey tool from Choosing Wisely Canada to identify which interventions may best suit your hospital. Complete the survey together, or have all team members complete it individually to see whether responses vary across team members.
2 Society for the Advancement of Blood Management, Inc. Transfusion overuse: exposing an international problem and patient safety issue [Internet]. Mount Royal (NJ): The Society. 2018 Aug [cited 2021 Nov]. Available from: https://www.sabm.org/assets/pdfs/SABM-Transfusion-Overuse-2019.pdf
3 Mehta N, Murphy MF, Kaplan L, Levinson W. Reducing unnecessary red blood cell transfusion in hospitalized patients. BMJ. 2021;373:n830.
4 Guttendorf J. Implementing restrictive transfusion strategies to improve patient outcomes [Internet]. Morrisville (NC): Critical Care Alert, Relias Media. 2018 Mar 1 [cited 2021 Nov]. Available from: https://www.reliasmedia.com/articles/142226-implementing-restrictive-transfusion-strategies-to-improve-patient-outcomes
This indicator measures the proportion of hospitalized patients who receive at least 1 sedative-hypnotic medication. We provide 2 versions of the indicator to capture patients who presumably were not receiving sedative-hypnotic medication before admission and all patients who receive a sedative-hypnotic during hospitalization. For more information, see OurPractice General Medicine: Background and Indicator Details.
Sedative-hypnotic medications such as benzodiazepines and “z-drugs” can cause confusion and are known to increase the likelihood of delirium; they are not recommended as first-line therapies for symptoms such as insomnia, agitation, or delirium. To mitigate the risk of hospital-acquired delirium, Choosing Wisely Canada recommends that health care teams use caution when prescribing these medications and consider nonpharmacological therapies for hospitalized patients.1,2 While it may not be possible to completely avoid prescribing sedative-hypnotic medications, clinicians should prescribe medications based on each patient’s unique needs and consider alternatives to sedative-hypnotics when possible.
Table 1 provides change ideas to reduce the prescribing of sedative-hypnotics known to increase the risk of delirium. See Appendix A for the list of medications included in this indicator.
| Change Idea |
|---|
| Identify and remove “routine” nighttime sedative-hypnotic orders from admission order sets (led by the local physician lead via standard hospital approval processes) |
| Collaborate with local pharmacists to identify new sedatives initiated for sleep and to avoid the administration of nonessential medications during sleep hours (22:00–06:00) |
| Understand prescribing patterns and identify the medications prescribed most frequently for sleep (i.e., are they associated with incidences of hospital-acquired delirium?) |
| Use nonpharmacological strategies to help patients sleep |
| Engage an interdisciplinary team, including clinicians, delirium and geriatric specialists, administrative staff, environmental services, patients, and care partners, to create a sleep-friendly environment (e.g., minimize sleep interruptions; offer patients warm beverages, eye masks, and earplugs; reduce noise; keep lights low). For further details, see the Delirium Aware: Safer Health Care (DASH) Implementation Toolkit |
| Participate in Ontario Health’s Delirium Aware: Safer Healthcare (DASH) Campaign, a 3-year campaign designed to strengthen the ability of hospital teams across Ontario to prevent, identify, and manage hospital-acquired delirium |
As you review Table 1, consider the following reflective questions and suggested actions:
Are sedative-hypnotics commonly prescribed for sleep in your hospital?
Working together, identify which medications are prescribed most frequently for sleep and when they are prescribed (e.g., during overnight hours, when a most responsible physician may not be available). Could the inclusion of sedative-hypnotics in order sets lead to unnecessary prescriptions for sleep?
Does your team have a plan to review your OurPractice General Medicine Report data, conduct audits, and engage in other self-reflection activities to identify, verify, and correct underlying reasons for unnecessary sedative-hypnotic prescribing for sleep?
When conducting a root cause analysis, ask yourselves the following questions:
a. What is causing variation in sedative-hypnotic prescribing for sleep in my unit/ward/department?
b. Which practices or processes (or lack thereof) are contributing to unnecessary sedative-hypnotic prescribing for sleep?
c. What is working well to reduce unnecessary sedative-hypnotic prescribing for sleep, and how can we do this more often?
Which colleagues in other teams or divisions (e.g., nursing, pharmacy) can you collaborate with to reduce unnecessary sedative-hypnotic prescribing for sleep?
Which change ideas are most relevant to your general medicine teams? Which are most feasible to implement?
2 Ontario Health. Delirium: care for adults [Internet]. Toronto (ON): Queen’s Printer for Ontario; 2021 [cited 2024 Jan 24]. Available from: https://www.hqontario.ca/Portals/0/documents/evidence/quality-standards/qs-delirium-quality-standard-en.pdf
The OurPractice: General Medicine Reports were co-designed by Ontario Health and GEMINI, with the support of a guidance committee comprising physicians, subject matter experts, hospital administrators, and quality improvement leaders. These reports enable hospitals to confidentially see their general medicine clinical care patterns compared to those of their anonymized hospital peers participating in GeMQIN. The OurPractice: General Medicine Report can be used in conjunction with the physician-level MyPractice: General Medicine Reports to highlight areas of consistent high-quality care, while also identifying opportunities for improvement.
This OurPractice: General Medicine Report was sent only to your hospital's GeMQIN leads and will not be shared with any agencies or physician groups.
Administrative and clinical data extracted from your hospital's electronic medical records systems were used to generate this report. Administrative databases that were used include: the National Ambulatory Care Reporting System (NACRS) database; the Discharge Abstract Database (DAD); and the Admission Discharge Transfer System (ADT).
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For more information about OurPractice: General Medicine Reports, please email us at GeMQIN@OntarioHealth.ca.