Savana Manager
An analytical tool that generates Graphs and tables that describe professionals’ activity
Try Savana Manager nowFeatures
- Detection and quantification of variability in clinical practice
- Accelerate clinical trial feasibility by finding patients that fulfil increasingly complex inclusion criteria thanks to free text mining.
- Benchmarking clinical practices among different points of care.
- Find and describe a treated target population with higher granularity and in real time.
Benefits
Generates immediate and intuitive dynamic tables and graphs that granularly describe the activities carried out by clinicians. In addition, it makes it possible to determine the epidemiological distribution of the treated population. It identifies information that was untapped until now and otherwise impossible to extract
Clinical practice variability
Population
epidemiology
How Savana Manager is helping
Real examples from Savana users:
- Avoid the use of unnecessary elastic packs (surgery records)
- Discover that the most frequent point of contact after Alzheimer’s diagnosis is Traumatology
- Say whether new oral anticoagulants are safer than Warfarin in atrial fibrillation
- Detect candidates who had been wrongly disqualified from undergoing Parkinson surgery
- Correct a 2x error (made with ICD) in the prediction of beds and salbutamol for bronchiolitis
- Identify patients with refractory essential tremor who could be treated with ultrasound (an underused technology until now)
- Call in patients with family aortic cardiomyopathy (ICD code unavailable) for clinical trials
- Identify all patients who received non-invasive mechanical ventilation
- Determine how many of the women giving birth come back to the same hospital
- List how many cytoreductions were performed by Dr. X
- Count how many cases of bronchiolitis were incorrectly referred to pediatric ICU
- Anticipate how many spinal surgeries can actually be prevented with back school
- Quantify the number of cases of suspected appendicitis in which computerized tomography + abdominal ultrasound were used
- Detect nosocomial infections
- Find out how many breast cancers were treated with lapatinib
- Identify pre-diabetic patients and hypoglycemic events for a clinical trial
- Find out how many patients with ischemic cardiopathy were discharged without statins
Journey of diabetic patients at a tertiary hospital
Combines primary and specialized care
Text and analytic data
Structured and unstructured information
Evolution of treatment for multiple sclerosis at a hospital in Madrid
- Combines primary and specialized care
- Text and analytic data
- Structured and unstructured information
