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Savana Manager

Generates immediate and intuitive graphs and tables that describe professionals’ activity

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Features

  • Detection and quantification of variability in clinical practice
  • Description of the treated population with higher granularity than structured records

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

    Clinical practice variability

  • Epidemiología poblacional

    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

  • Combine primary and specialized care

    Combines primary and specialized care

  • Text and analytic data

    Text and analytic data

  • Structured and unstructured information

    Structured and unstructured information

Evolution of treatment for multiple sclerosis at a hospital in Madrid

  • Combine primary and specialized care

    Combines primary and specialized care

  • Text and analytic data

    Text and analytic data

  • Structured and unstructured information

    Structured and unstructured information

See our success stories

Discover the benefits of using Savana

See our success stories

Are you interested in Savana Manager?

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