CASE-CANCELLATION
Description
Surgical Same Day and Outpatient Case Cancellation Predictor
Solution
This ML model predicts patients with a high likelihood to cancel their procedure or follow up outpatient and ambulatory appointments. The model generated $500,000 in retained revenue in the first 6 months of deployment.
Service Line
Perioperative
Published Outcomes
https://www.sciencedirect.com/science/article/abs/pii/S0952818022003452?via%3Dihub
SEPSIS-REALTIME
Description
Real-time Severe Sepsis Detector (bundle w Sepsis LLM)
Solution
Identifying patients at high risk of developing organ damage and multi-organ failure due to Sepsis in timely manner (prior to clinician recognition). Results show industry-leading performance for false-positive rates (<3%).
Service Line
Respiratory
Published Outcomes
https://link.springer.com/article/10.1186/s13054-018-2194-7
SEPSIS-EARLY
Description
Severe Sepsis Early Recognition Predictor
Solution
Identifying and setting the time of presentation (ToP) and onset of the disease during patient hospitalization, in patients that meet the automated CMS Sep-II criteria during their hospitalization (see industry-leading performance vs other leading sepsis models below).
Service Line
Critical Care
Published Outcomes
https://www.sciencedirect.com/science/article/abs/pii/S0952818022003452?via%3Dihub
PACU-LOS
Description
PACU Length-of-Stay Predictor
Solution
PACU-LOS prevents prolonged PACU stays after surgery by predicting the length of stay in PACU post surgery to optimize sequencing the operations and PACU utilization post surgery.
Service Line
Perioperative
Published Outcomes
https://link.springer.com/article/10.1186/s13054-018-2194-7
CASE-CANCELLATION
Description
Surgical Same Day and Outpatient Case Cancellation Predictor
Solution
This ML model predicts patients with a high likelihood to cancel their procedure or follow up outpatient and ambulatory appointments. The model generated $500,000 in retained revenue in the first 6 months of deployment.
Service Line
Perioperative
Published Outcomes
https://www.sciencedirect.com/science/article/abs/pii/S0952818022003452?via%3Dihub
SEPSIS-REALTIME
Description
Real-time Severe Sepsis Detector (bundle w Sepsis LLM)
Solution
Identifying patients at high risk of developing organ damage and multi-organ failure due to Sepsis in timely manner (prior to clinician recognition). Results show industry-leading performance for false-positive rates (<3%).
Service Line
Respiratory
Published Outcomes
SEPSIS-EARLY
Description
Severe Sepsis Early Recognition Predictor
Solution
Identifying and setting the time of presentation (ToP) and onset of the disease during patient hospitalization, in patients that meet the automated CMS Sep-II criteria during their hospitalization (see industry-leading performance vs other leading sepsis models below).
Service Line
Critical Care
Published Outcomes
https://www.sciencedirect.com/science/article/abs/pii/S0952818022003452?via%3Dihub
PACU-LOS
Description
PACU Length-of-Stay Predictor
Solution
PACU-LOS prevents prolonged PACU stays after surgery by predicting the length of stay in PACU post surgery to optimize sequencing the operations and PACU utilization post surgery.
Service Line
Perioperative
Published Outcomes
AI/ML Models & ExplainerAI Dashboards
- Sepsis Early Warning Detector
- Sepsis Realtime Phenotyping
- SEPSIS-LLM
- Case Cancelation: Outpatient Visit No-Show Prediction
- Case Cancellation: Surgical Same Day Cancellation
- Clinical Trial Matching LLM
- De-Identification & PHI Redaction LLM
- Automated Chart Review & Deep QA LLM
- Automated Concept Extraction & Encoding
- Risk Score to Predict Readiness for Surgery
- Pediatric Acute Chest Syndrome Predictor
- PACU Length of Stay predictor
- Acute Respiratory Distress Syndrome Predictor
- Post Operative Respiratory Failure Prediction
- Respiratory Risk Early Warning
- Model to Predict Risk of Cancer Metastasis to the Spinal Chord
- ExplainerAI Analytics Platform
Pediatric Acute Chest Syndrome Predictor (ACS-PS-RISK)
Problem
Acute Chest Syndrome (ACS) presents a significant health challenge in pediatric sickle cell disease (SCD), accounting for 15-30% of all pediatric SCD hospital admissions. Each episode of ACS not only increases the risk of morbidity and mortality but also poses a threat of irreversible lung damage. Despite known risk factors such as a history of asthma, ACS often arises unexpectedly, complicating patient management and increasing the burden on healthcare systems. The unpredictability of ACS exacerbates the need for enhanced monitoring and preventive strategies in hospital settings, leading to increased use of medical resources and extended hospital stays.
Solution
This advanced Machine Learning model excels in predicting ACS with high accuracy in a pediatric cohort. By identifying high-risk patients early, we can significantly enhance treatment options and improve inpatient management strategies, ensuring better patient outcomes.
Outcome
Our state-of-the-art random forest model predicts ACS 24 hours before chart diagnosis with an impressive sensitivity of 82.5% and a specificity of 61.3% at a 0.17 cutoff point. It boasts a remarkable 93% negative predictive value and a 34% positive predictive value.
Service Line
Pediatrics
Risk Score to Predict Readiness for Surgery (ASA-PS-RISK)
Problem
The American Society of Anesthesiologists Physical Status (ASA-PS) classification system, established in 1941, is instrumental in evaluating a patient’s pre-anesthesia medical comorbidities. However, its application has revealed significant challenges, primarily due to its reliance on subjective human judgment. Variability in assessments among anesthesiologists and consistent underestimation by surgeons and their teams can lead to inaccurate patient classifications. This inconsistency can adversely affect the allocation of healthcare resources and the planning of preoperative evaluations, potentially leading to unnecessary testing, operative delays, or even cancellations on the day of surgery. As such, there is a clear need for a more reliable method to assign ASA-PS classifications to enhance the accuracy and effectiveness of perioperative risk management.
Solution
We developed a machine learning physical status model using preoperative data, improving early identification of high-risk patients. This model standardizes preoperative evaluation, enhancing outcomes for ambulatory surgery patients.
Outcome
Our study showed that the anesthesiologist ASA-PS and ML-PS agreed 57.2% of the time. The ML-PS more often assigned patients to extreme ASA-PS categories (I and IV) and fewer to II and III (p<0.01). Both systems had excellent predictive values for 30-day mortality and good values for postoperative ICU admission and adverse discharge. Among 3,594 patients who died within 30 days post-surgery, ML-PS reclassified 1,281 (35.6%) into a higher risk category than anesthesiologists did. However, for patients with multiple co-morbidities, anesthesiologist-ASA-PS had better predictive accuracy.
Service Line
Perioperative
Published Outcomes
Acute Respiratory Distress Syndrome Predictor (ARDS-RISK)
Problem
Acute respiratory distress syndrome is dangerous, especially in critical care and was the primary cause of death for COVID patients
Solution
The early recognition ML model predicts ARDS risk factors and identifies patients, allowing care teams to intervene sooner and prevent poor or worsening outcomes.
Service Line
Respiratory
Published Outcomes
Automatic Chart Abstraction and CPT Code Generation (AUTOCHART-LLM)
Problem
Hospitals are mandated by regulatory and standards bodies to report quality and outcomes data. Signifiant time and expense is spent annually by hospitals on nursing abstraction services for purposes of reimbursement, board certifications, value-based care and population health reporting.
However, manual chart review by health-system quality teams and nurses is both costly and inefficient for several reasons. First, it requires significant labor, with skilled professionals spending extensive time examining and interpreting large volumes of patient records. Second, the process is prone to human error, which can lead to inconsistencies in data extraction and analysis. Third, it lacks scalability, as increasing patient volumes directly translate to increased labor and costs. Finally, manual reviews often result in delayed insights, slowing down the implementation of necessary quality improvements and adjustments in patient care practices.
Solution
Our innovative solution enhances manual chart review by utilizing a fine-tuned Large Language Model to automatically extract and link key concepts, like CPT codes, from complex medical notes, streamlining the process and ensuring 94%+ accuracy. The model can be tuned to search for any key concept. See our Clinical Trials Matching LLM for another example of this technology in use.
Outcome
Nurse data abstraction from patient charts, determining the appropriate CPT codes from clinical notes and data entry into portals or registries for numerous quality and performance reporting needs creates significant operational overhead for health systems. Using our AUTOCHART LLM, sometimes as part of an ensemble with our other LLMs, can fully automate most chart abstraction needs.
In one use case, the ACS National Surgical Quality Improvement Program, our LLM and ETL solution 100% eliminated data abstraction/extraction and data entry time, auto-generated CPT codes from clinical notes and automatically entered discrete data into the appropriate fields within the ACS registry, leaving only the last step of validation to be performed by staff. 5 year projections show a potential for $700K-$1M in cost savings, significant ROI and better utilization of depleted nursing resources.
Service Line
Quality, Revenue Cycle, Population Health, Accreditation
De-Identification & PHI Redaction LLM (DEID-LLM)
Problem
In daily health-system operations, there are various groups that need to see clinical text generated by treatment teams, but to ensure privacy and regulatory compliance may only see this data in a de-identified manner. Examples include research, billing, revenue cycle, QI/PI, population health and various other operational or reimbursement purposes and, especially, where an outside entity is the one reviewing the data.
Solution
The DEID-LLM allows for real-time, large-scale de-identification of clinical text. Once processed, the de-identified text can be immediately utilized by downstream workflows, internally or externally of institution, as required. Integrated within a business process workflow, this LLM has the potential to produce significant ROI for health systems. A potential solution for cybersecurity, privacy and compliance teams or those teams which have data governance or oversight responsibilities.
Service Line
Hospital-Wide
Case Cancelation: Outpatient Visit No-Show Prediction
Problem
For health systems, missed appointments in ambulatory settings represent a significant challenge. Every time a patient fails to show up for a scheduled visit, a valuable appointment slot goes unused. This not only leads to lost revenue for healthcare providers but also denies urgent care to other patients who could have benefited from that slot. Moreover, patients who miss their appointments miss out on critical care opportunities, compromising their health outcomes.
Solution
Building on our Surgical Case Cancellation model, Cognome developed a Machine Learning algorithm to predict outpatient visits no-shows and patient cancellations, in order to allow health-system to optimize available appointment time-slots.
Service Line
Case Cancellation: Surgical Same Day Cancellation
Problem
Solution
Cognome developed a Machine Learning model to predict case cancellations within 24 hours of surgery. This model utilizes insights from 29 predictors, integrated into the EHR, allowing case managers to proactively address medical, behavioral health, transportation or SDOH issues in order to optimize clinician and resource utilization.
Outcome
This model is one of the longest running, active ML models in healthcare showing $500K-$1M dollars in retained revenues and cost savings annually at a large health system. The model showed good discrimination in the development cohort with an AUC of 0.79 (95% confidence interval 0.79 - 0. 80) and good discrimination in the validation cohort with an AUC of 0.73 (95% confidence interval 0.72-0.73).
Service Line
Perioperative
Published Outcomes
CLINICALTRIALS-LLM
Problem
Significant challenges exist in clinical trial operations, specifically the efficient identification, matching, and recruitment of patients, including those from underserved populations. The process is very cumbersome and rarely timely which is a problem for getting patients started in clinical trials, especially in Oncology, where time to treatment literally is life and death.
Solution
Our LLM consumes large volumes of publicly available data on clinical trials and then using our Automated Chart Review LLM (AUTOCHART), it matches the trials to patients using all inclusion and exclusion criteria. Our systematic evaluations show that our automation solution can accurately predict complex, criterion-level eligibility with faithful explanations significantly reducing staff time and resource requirements to recruit for and manage clinical trials. This allows health systems to increase the volume of clinical trials they can facilitate with their pharma and life sciences partners.
Service Line
Hospital-Wide
PACU Length of Stay predictor (PACU-LOS)
Problem
Health systems often face inefficiencies in PACU (Post-Anesthesia Care Unit) scheduling, leading to prolonged stays that strain resources and increase costs. This issue intensifies with complex cases, where the time spent in the PACU often exceeds initial estimates. Afternoon surgeries, in particular, are at increased risk of extended stays that may require unexpected inpatient admissions. These issues disrupt surgical schedules and impose significant financial burdens, including increased staffing demands and the costs of overnight care.
Solution
We have developed and validated a preoperative prediction ML model for prolonged PACU-LOS after ambulatory surgery. This model helps optimize PACU utilization, enhancing efficiency and cost-effectiveness.
Outcome
Our model demonstrated outstanding discriminatory ability across development, internal, and external validation cohorts, achieving areas under the receiver operating characteristic curve of 0.82, 0.82, and 0.80, respectively. For surgeries starting in the afternoon, PACU-USE scores of ≥43 predicted a 32% risk of PACU stays past 8 PM, compared to just 8% for lower scores (p<0.001). This resulted in a higher direct PACU cost of care of $207 (p<0.001). The benefits of using the PACU-USE score were especially significant in freestanding ASCs without PACU bed limitations.
Service Line
Post Operative Respiratory Failure Prediction (RESPIRATORY-POSTOP)
Problem
Postoperative respiratory failure remains a critical issue in surgical care, associated with significantly increased odds of mortality, higher healthcare costs, and diminished quality of life. Despite ongoing research and various definitions of respiratory failure by notable healthcare organizations, the incidence rate of this complication has not improved in the past two decades, hovering between 1–4%. Existing prediction tools for postoperative respiratory failure suffer from limitations such as poor performance, narrow applicability, and manual calculation requirements, which hinder their widespread implementation. These tools often fail to integrate modern machine learning techniques, which can more effectively handle complex data interactions and offer improved accuracy.
Solution
This machine learning prediction model has set a new standard for accurately identifying postoperative respiratory failure, enhancing patient outcomes and improving healthcare quality metrics.
Outcome
Our cutting-edge ML model outperforms existing tools, demonstrating superior accuracy with an AUROC of 0.93, compared to 0.82 for ARISCAT and SPORC-1. At 80-90% sensitivities, our model achieves a higher positive predictive value (11%) and a lower false positive rate (12%) compared to ARISCAT and SPORC-1.
Service Line
Respiratory
Published Outcomes
Prediction of Prolonged Ventilation (RESPIRATORY-RISK)
Problem
Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation would enable earlier evaluation and intervention leading to improved outcomes.
Solution
We developed and validated an ML model integrated within the EHR to identify patients at risk of respiratory failure.
Outcome
The hospital mortality for patients on mechanical ventilation >= 48 h was 33% in both 2013 and 2017 compared to mortality rates of 1.4% in 2013 and < 1% in 2017 for patients with MV < 48 h.
Service Line
Respiratory
Published Outcomes
Sepsis Early Warning Detector (SEPSIS-EARLY)
Developed in Conjunction with Intel
Problem
Sepsis, a life-threatening condition characterized by organ dysfunction in response to infection, presents a critical challenge for health systems due to its complex detection and rapid progression. Early recognition and treatment of sepsis are vital for improving patient outcomes, yet there is no definitive test for its early detection. Clinicians must rely on a combination of vital signs, biomarker values, and alarming symptoms to assess a patient's risk, a process fraught with potential delays and inaccuracies. With the incidence of sepsis resulting in high mortality rates and substantial healthcare costs, there is an urgent need for more effective predictive tools.
Solution
Our study marks a significant breakthrough in early sepsis detection using advanced Machine Learning models. Sepsis, a life-threatening condition, often lacks a specific diagnostic test, making early recognition challenging. Leveraging live clinical data, our model predicts sepsis onset up to 6 hours in advance, providing a crucial tool for timely intervention and improved patient outcomes. By combining various scoring systems, lab values, and demographic information, our machine learning approach significantly enhances sepsis prognosis, ultimately reducing mortality rates and improving patient care.
Outcome
Our 2% false-positive rate is the highest performing of all currently published models. The XGBoost model trained on a large, live patient dataset demonstrates exceptional predictive capabilities with a normalized utility score of 0.494 on test data and 0.378 on prospective data at a 0.3 threshold. It also achieved an impressive F1 score of 80% on test data and 67.1% on prospective data, highlighting its potential for clinical integration.
Service Line
Hospital-Wide
SEPSIS-LLM
Problem
The lack of Sepsis-specific LLM training data, the numerous systems which house data markers required to accurately asses Sepsis and the volume of patients on any given day in a typical hospital make predicting and optimizing the management of Sepsis very challenging.
Solution
Bundled with our Sepsis ML model, our computable phenotyping LLM uses clinical notes and other sources of data to identify severe sepsis based on large volumes of provider documentation of patient progress and clinical conditions across several systems in real-time and integrated within the clinical workflow.
Service Line
Hospital-Wide
Sepsis Realtime Phenotyping (SEPSIS-REALTIME)
Problem
Existing methods of defining severe sepsis are problematic for several reasons. They are dependent on diagnosis codes, which are only available after patient discharge, limiting timely intervention. The discovery process often relies on chart abstraction, which is labor-intensive and inefficient. Automated methods tend to be inaccurate, generating a large number of false positives; for instance, some models erroneously indicate that 25% of patients have sepsis. Additionally, no current methods effectively utilize clinical text, and the existing models are not practically useful in real-world clinical settings.
Solution
Our custom-built phenotype offers a fully automated, clinically validated solution that accurately reflects the actual prevalence of sepsis in the hospital. It can be deployed in real time and leverages clinical notes for enhanced accuracy.
Service Line
Hospital-Wide
Published Outcomes
Model to Predict Risk of Cancer Metastasis to the Spinal Chord (ONCOLOGY-SPINAL)
Problem
Patients who developed spinal metastesis weren't being evaluated fast enough by oncologists.
Solution
Our cutting-edge NLP model demonstrates exceptional accuracy in predicting spinal tumors and MECC in spine MRI reports. Integration within the EHR ensures faster referrals to specialists. This advancement promises to reduce morbidity and increase survival rates, significantly enhancing patient outcomes.
Outcome
Out of 37,579 radiology reports reviewed, 36,676 were labeled negative and 903 were identified with MECC. By setting a positive result cutoff at 0.02 to minimize false negatives, we achieved a 100% sensitivity rate and a remarkably low false positive rate of 2.2%.
Service Line
Oncology
Automated Concept Extraction and Encoding Services for Billing, Reporting, QI & PI
Problem
Solution
Our solution revolutionizes the automated extraction of key concepts from complex clinical notes. By leveraging a systematic and standardized approach, it surpasses current state-of-the-art extraction algorithms in both performance and generalizability.
Service Line
Quality
ExplainerAITM Analytics Dashboard Platform
Performance Insights, Management and Governance for AI / ML Models
Description
ExplainerAI™ is a first-of-its-kind open analytics and performance management platform that provides a single pane-of-glass view into your portfolio of AI and ML models. Predictive risk factors, algorithm configuration parameters, performance analytics are presented in real-time at the patient and population level.
ExplainerAI™ dashboards are where all measures are aggregated and display model performance at-a-glance, enabling users to effectively identify patient care gaps for each measure and act accordingly. Users can drill down to better understand model configuration, at very granular levels, and performance over time.
Built to foster adoption through trust, ExplainerAI™ is designed on the four pillars of NIH’s AI Governance Framework: trustworthiness, fairness, transparency and accountability.
An ExplainerAI™ dasboard aggregates model data and breaks it down by demographic, sociographic and other key measures to ensure that there are no biases towards race, gender, economic group or other cohort that may be inadvertently disadvantaged by the model or its operational implementation. Explainer AI™ can be configured to map to an organization’s AI governance requirements for explainability.
Extendable to Power BI, Tableau and other BI platforms.