The Alexandria Health Library

Breathing Life Into Clinical Decision Support Tools

Contact: jordan@alexandria.health

Advisory Panel

Advisory Panel Member
Wip
Advisory Panel Member
Wip
Advisory Panel Member
Wip
Advisory Panel Member
Wip

Tools

Emergency Surgery

POTTER

Virtual Grand Rounds / Calculator / Tree
Problem Statement

Understand the risk and potential outcomes in patients with acute surgical problems in need of an emergency operation

Constraints

Given this is a life or death situation:

  • The clinician cannot hand off accountability of the decision
  • Thus, the clinician must be able to inspect the algorithm’s logic
  • Upon inspection, the clinician must recognize the pathways/branches of the algorithm
  • A good outcome is when the clinician recognizes the outcome; POTTER provides precision

Cerebrovascular Health

OCT-FSRS (Optimal Classification Tree – Framingham Stroke Risk Score)

Virtual Grand Rounds / Calculator / Tree
Problem Statement

Predict likelihood of stroke within the next ten years that recognizes non-linear interactions of risk factors in order to support medication recommendations

Constraints

This tool ultimately is used to influence patient behavior over the long-term. Accuracy is critical. Also, important is that the tool support a doctor to conduct patient counselling. Thus, the doctor must be able to guide the patient to understand what factors are driving risk, and of those factors which are controllable by either medication intervention or lifestyle changes and which are genetic. Thus, the doctor must be positioned to describe the set of risk factors that led the patient to the current likelihood prediction, or the predictor risks having limited ability to influence behavior.

Oncology

OncoMortality

Virtual Grand Rounds / Calculator / Tree
Problem Statement

As treatment options evolve, an oncologist’s decision logic is becoming increasingly complex (e.g. how can a provider keep up with the numerous novel therapies introduced every year?). And, reliance on intuition-based assessment of treatment risk and overall mortality will become less useful (i.e. an oncologist relying on the same set of therapies for every patient irrespective of their individual risk factors is not good enough). ONCOMORTALITY provides a prediction tool to identify patients with high mortality risk to guide the decisions on whether to:

  • Maintain or modify treatment
  • Optimize for quality of life
Constraints

Treatment planning in cancer is highly emotional, sensitive, and personal. Thus, it is critical for oncologists to fully understand the logic of the clinical decision support tool and trust the cohort assignment.

Trauma

Closed Head Injury

Virtual Grand Rounds / Calculator / Tree
Problem Statement

When children suffer traumatic injury to the head, both clinician and parents have an inclination to test for clinically important traumatic brain injury (ciTBI). A provider has the option to recommend:

  • A computed tomography (CT) scan, which exposes the patient to ionizing radiation and is costly, or
  • Recommend observation, which carries the risk of not treating an emergency issue

The current Pediatric Emergency Care Applied Research Network (PECARN) rules use simple and easy to memorize rules to assess the likelihood of ciTBI.

Thus, the problem statement is to improve the Pediatric Emergency Care Applied Research Network (PECARN) rules’ predictive accuracy to identify children at very low, intermediate, and high risk of clinically important traumatic brain injury (ciTBI).

Patients without substantially-altered mental status, e.g., with Glasgow Coma Scale (GCS) scores of 14 or 15, rarely suffer from clinically important traumatic brain injury (TBI) or have evidence of intracranial injury with CT imaging. Avoiding needless CTs in such patients is highly desirable.

Constraints

PECARN has developed and validated rules for identifying children with head trauma but without substantially-altered mental status that are at low risk of clinically important TBI and should not receive head CT. The PECARN rules are widely used and have been independently validated multiple times.

  • Given the challenging trade-off between immediate testing and waiting, our solution must be as interpretable and familiar to clinicians as PECARN and be more accurate.
  • The solution must be as readily available as the current easy to memorize and apply PECARN rules and be able to not only be interpreted by clinicians, but also the parents of the children.

Urology

PredictVUR

Virtual Grand Rounds / Calculator / Tree
Problem Statement

When a urinary tract infection is presented in a pediatric patient, pediatricians and urologists will seek to identify if the patient is at risk of follow-up urinary tract infection and/or vesicoureteral reflux (VUR), a condition in which urine flows retrograde from the bladder into the ureters/kidneys. If the patient is at risk of VUR, the clinician will order an invasive diagnostic test called a voiding cystourethrography (VCUG). The objective is to quantify the risk of VUR

Constraints

Once a urinary tract infection is identified, there is bias between medical specialties in diagnostic method. Whereas pediatricians tend to favor observation, urologists tend to favor performing a voiding cystourethrography (VCUG). In addition to this, while the data used to support this clinical decision support tool is from two well-known urology studies (the randomized intervention for children with vesicoureteral reflux [RIVUR] and careful urinary tract infection evaluation [CUTIE]), there will be situations in which these two evidence based medicine trials are not 100% applicable and a given pediatrician / urologist provider team will have to engage their intuition. Thus, not only must this clinical decision support tool be highly accurate, it must expose the logic of the methodology in order to facilitate a dialogue between a pediatrician and a urologist and help them work through their natural, medical field-specific respective biases.

Organ Transplantation

OPOM

Virtual Grand Rounds / Calculator / Tree
Problem Statement

The successful clinical application of liver transplantation has generated a discrepancy between supply and demand, thereby generating a persistent insufficient organ supply that results in thousands of candidate deaths every year while candidates await liver transplantation.

Given the scarcity of this resource, the Model for End-Stage Liver Disease (MELD) accurately prioritizes a waitlisted candidate’s likelihood of death within the near future, so that the limited supply of donated livers can be allocated to maximize the benefit from transplantation. The MELD score was updated in 2014 and 2015.

The objective of OPOM is to improve the accuracy of the MELD method and include hepatocellular carcinoma (HCC) patients, and then demonstrate effectiveness with simulation.

Constraints

The first order requirement to implement an allocation methodology is to achieve alignment on methodology. For this methodology:

  1. Determine the probability that a patient will either die or become unsuitable for liver transplantation within three months, given his or her individual characteristics
  2. Allocate limited supply of donated livers to the patients that need it and then maximize benefit of transplantation

Interpretability and transparency is required to achieve alignment

Surgery

Elective Surgery

Virtual Grand Rounds / Calculator / Tree
Problem Statement

Support a go / no go decision for emergency surgery that recognizes non-linear interactions of risk factors

Constraints

Given this is a life or death situation:

  • The clinician cannot hand off accountability of the decision1
  • Thus, the clinician must be able to inspect the algorithm’s logic
  • Upon inspection, the clinician must recognize the pathways / branches of the algorithm
  • A good outcome is when the clinician recognizes the outcome; POTTER provides precision

Obstetrics

Risk Adjusted C-Section Rate

Virtual Grand Rounds / Calculator / Tree
Problem Statement

In the United States, there is general consensus amongst the maternal fetal medicine (MFM) and the obstetrics and gynecology (OBGYN) communities that the C-Section rate is too high. Currently hospitals publish their C-Section rate and there is often variance in hospital performance. Due to the fact that hospitals see different patient populations, these benchmarks are only marginally effective. Can we create a benchmark that takes into account different patient populations?

Constraints

Given the potential use of such a benchmarking tool (e.g. establishing insurance / provider incentive schemes), the method must be auditable

Obstetrics

Second Stage Predictor

Virtual Grand Rounds / Calculator / Tree
Problem Statement

On the labor and delivery floor, a provider team can either continue expectant management or make an intervention. Guidelines are ambiguous and there is risk with either option. Given a patient in second stage, provide morbidity likelihood for expectant management and morbidity likelihood for an intervention for both mother and child.

Constraints

In the majority of situations, the patient will need to give consent. Thus, any clinical decision support tool must be understandable to a patient undergoing trial of labor.

Endorsement
Conf smfm

Oral presentation presented at the Maternal Fetal Medicine Conference in Feb 2019

Tool Status
Pause

Pilot participating residents reported that the model felt the questions were not patient focused, and thus the tool did not feel useful. Senior medical author reported necessity of including fetal station in the analysis. Thus, work paused.