Harm Prevention
Elderly Woman Home Visit for Avoidable Regression
Sepsis: Avoidable Health Regression
By identifying this cancer patient as at risk of mortality, her physician immediately brought her in for an office visit. Despite no signs or complaints, bloodwork revealed that the patient was septic. She was rushed to the hospital where she was treated and released.

An 85-year-old breast cancer patient who lives alone was identified by the Jvion Machine as at risk for 30-day mortality. Her physician, who was on a plane at the time, received an alert. The physician immediately engaged her team to contact the patient and bring her in for an exam. The Case Management team was also engaged to reach out to the patient and ensure transportation.

Despite no complaints or outward signs of sepsis, the clinical team ordered bloodwork. The patient was sent back home with her friend who drove her to the clinic. Later that same day, bloodwork revealed that the patient was showing signs of sepsis. At about the same time, her friend contacted the clinic because the patient had fainted. She was immediately sent to the ED where she was diagnosed with sepsis and admitted to the hospital.
Because of the Jvion Machine’s insights, this patient’s sepsis was caught early, which helped stop the progression of the infection. Moreover, the right resources—clinical staff, friends, and social supporting including transportation—were engaged to prevent what could have been a deadly event.
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Elderly Woman with Head Pain

Discharge Optimization: Nutritional Needs

By highlighting that this patient likely lives in a food desert, clinicians were able to address her immediate nutritional need and avoid an extended stay in the hospital.

A 68-year-old woman was admitted to the hospital after a fall that resulted in a mild head injury. She was previously diagnosed with subclavian steal, and her chief complaint was of nausea/vomiting that had lasted six days. She refused to eat.

The Jvion MachineTM identified the patient as at risk for an extended length of stay (LOS) because of the strong likelihood that she in lives in a food desert, is low income, and has a history of fluid/electrolyte imbalance. Recommendations included a nutrition consult and oral nutritional supplements.

Based on the insights delivered by the Jvion Machine, clinicians ordered nutritional supplements and consulted the dietary team to ensure that the patient’s nutritional needs would be met pre-op and post-operatively. As a result, the patient avoided a prolonged stay in the hospital and received the ongoing support she needed.

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Discharge Optimization

Oncology 6-Month Depression
Behavioral Health: 6-month Depression
By identifying this patient’s challenges with medication compliance, Case Managers were able ensure medication adherence and lower the risk of depression.
A 71-year-old woman with breast cancer and a history of anxiety was flagged by the Cognitive Machine as high risk for depression within the next six months. One of the major contributors identified by the machine was the likelihood that this patient is challenged with medication compliance. A chart review confirmed that the patient had been prescribed anti-depressants in the past. However, the medication had not been discussed during recent visits indicating the potential for medication adherence gaps.

Based on the risks identified by the Cognitive Machine, a Case Manager connected with the patient and confirmed that she had not taken her anti-depressant for the past two weeks. A follow up appointment was immediately scheduled to discuss treatment options and the risk of a major depressive episode was reduced.

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Reducing Depression Risk

Identifying Patient at Risk

HAC: Pressure Injury Prevention

By identifying this patient who was on a trajectory toward high-risk, The Jvion machine directed nurses to apply primary prevention efforts that stopped the development of a pressure injury.
A 40-year-old female, patient was admitted to the hospital due to respiratory failure and was placed on an ICU unit. During a pilot study on nine inpatient units, this patient was flagged as high-risk  for a pressure injury. The patient did not clinically present herself as a high-risk patient to the nurse and was ranked as a low risk on the Branden scale (BMI within normal limits, adequate nutrition, young, continent of bowels/urine). However, the clinician followed Jvion’s recommendations and implemented a repositioning program, scheduled toilet-ing, ordered an air mattress, and restricted sodium.

Because of actions taken on the Cognitive Machine’s outputs, the pressure injury incidence at this hospital was reduced from 5.3% to 1.5% following the pilot. As a result of this success, the hospital is now expanding the vector to all inpatient units.

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Pressure Injury Prevention

Patient-Centric Care
Oncology: 30-day Mortality Risk
By identifying this patient’s risk of mortality, oncology practice Patient Care Coordinators were able to ensure appropriate symptom management and confirm advanced directives.
A 71-year-old woman with Stage III non-small cell lung cancer (NSCLC) was receiving a trial medication for her condition. The Cognitive Machine alerted the oncology practice’s Patient Care Coordinators that the patient was at risk for 30-day mortality and an avoidable hospital admission. Upon chart review, the care team realized that the patient had recently been admitted to the ER and was awaiting discharge to hospice. Without the Cognitive Machine, this information would not have made it to the Patient Care Coordinators because the referral to hospice was made by the hospital and not communicated to the oncology practice.
As a result, the care team was able to discontinue any trial medications, ensure appropriate symptom management while in hospice care, discuss and confirm the patient’s advanced directives, and reinforce the practice’s process of patient escalation to ensure patient-centered care.
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Oncology Vectors

Oncology End-of-Life Care

Oncology: End-of-life Care

By accounting for this patient’s current acute gastric issues, the Jvion machine identified a disconnect between the current care plan and the patient’s actual end-of-life care needs.

A 70-year-old woman with Stage IIIB Diffuse Large B-Cell Lymphoma was scheduled for an upcoming survivorship visit. This visit was scheduled based on the presumption that she was in remission from the cancer. However, the patient was experiencing acute gastric issues. The Jvion machine flagged this patient as at-risk for 30-day mortality and suggested a re-evaluation of the care plan. Upon chart review, the Patient Care Coordinator and Provider delayed the survivorship visit because of the possible progression of the disease within her gastrointestinal system. During the patient’s practice visit, the Patient Care Coordinator team delivered the supportive care needed and concurrent work-up for possible GI malignancy. 

As a result of the insights delivered by the Jvion machine and the actions taken by the Patient Care Team, the patient’s end-of-life plan was discussed earlier so that expectations were established, and the desires of the patient were accounted for.
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Oncology Vectors

Avoidable Admission Nurse Consultation

Hepatitis C: Avoidable Inpatient Admission

By accounting for an unknown medical history of gastrointestinal bleeding, the Jvion machine identified this patient as at-risk of an avoidable inpatient stay and helped the care team truly personalize interventions.
A 62 year-old man who was diagnosed with Hepatitis C and currently taking anticoagulant and antiplatelet drugs for the condition was flagged by the Jvion machine as at risk for an avoidable inpatient admission. While the patient should have received education on possible intestinal bleeding as part of his initial prescription, follow up regarding black and tarry stools was a leading recommendation delivered by the machine to help prevent the admission. Looking into the patient record, the clinical team found a history of inpatient admissions related to GI bleeding, no evidence of patient education, no recent documentation on follow-up or contact, and a lack of social resources. In addition to the machine’s recommendations for extensive patient education on his condition, medication side effects, and external signs of Hepatitis C, the clinician ensured that the patient made his follow up visit to his healthcare provider. The patient has not had an inpatient visit for 90 days since interventions were actioned.
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Avoidable Admissions

HIV Patient Recovering at Home
HIV: Avoidable Inpatient Admission
By identifying unknown risk factors, the Jvion machine determined that this patient was at risk for an inpatient visit related to a recurrent infection. The intervention recommendations provided by Jvion helped lower the patient’s risk and provided an important education opportunity.
A 39-year old woman who was diagnosed with HIV was identified as high-risk for an avoidable inpatient admission. The top recommended action was to check for a fever. The provider initially thought that this patient may be an outlier. However, with additional and extensive chart review, the team uncovered 13 admissions related to infections within the last two years. This risk was in addition to a history of non-compliance with medications, a lack of follow-up care from previous admissions, and a lack of social resources in the home. Based on the insights and individualized recommendations provided by the machine, this patient received critical education on how to identify signs of an infection including fever. As a result, this patient has not had an inpatient admission within the past 30 days.
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Chronic Condition Management

CHF Shortness of Breath

CHF: Avoidable Inpatient Admission

By uncovering a Congestive Heart Failure diagnosis that had fallen off this patient’s problem list, the Jvion machine ensured that the patient’s condition was treated, and risk of an avoidable admission was lowered.
A 50-year old man with no previous diagnosis and no current medications was identified as high-risk of an avoidable inpatient admission within the next 90-days. The risk factors highlighted Congestive Heart Failure (CHF) as a major contributor. Upon chart review, the team uncovered an echocardiogram in 2014 indicating a diagnosis of CHF. However, while the patient progress notes from the diagnostic in 2014 had a treatment plan, CHF was never added to the problem list. As a result, the patient had not received primary care or treatment for CHF. Without the Jvion machine, the clinicians would have overlooked this patient. They would have missed the opportunity to apply the recommended actions that ultimately helped control his CHF, reduce his risk of complications, and avoid a potential inpatient stay.
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Avoidable Admissions

Diabetes Glucose Check
Diabetes: Avoidable ER Visit
By understanding the health risks faced by this 45-year old diabetic male nurse who works night shifts, Jvion determined that he was at risk of an avoidable ER visit within the next 90 days. The machine provided the specific intervention insights that have kept him out of the hospital and his diabetes under control.
A 45-year old male nurse who works night shift and is currently diagnosed with hypertension, high cholesterol, and type 2 diabetes was identified by Jvion’s machine as high-risk for health regression. The care coordinator actioned on Jvion’s recommended interventions by providing education and guidance on mealtimes, exercises, and taking medications given a non-traditional work schedule. In addition, the care coordinator was able to connect the employee to a hospital-led diabetes support group and assistance with supplies needed to manage his diabetes.

Since the interventions, the employee has maintained his HbA1c <7% and has not had an admission or an ED visit within the past 6 months.
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Avoidable Admissions

Eldery CHF Patient Showing Doctor Appreciation
CHF: Readmission Prevention
By knowing that this 79-year old man was newly diagnosed with congestive heart failure (CHF), the Jvion machine gave the care team the information they needed to prevent a possible CHF readmission.
A 79-year old male patient was admitted for atrial fibrilization. Upon admission, the patient was identified by Jvion as high risk for readmission related to Congestive Heart Failure (CHF). Newly diagnosed with CHF, the admission was correlated to direct exacerbation of the disease. Jvion alerted the nurse on the transitional care team who reviewed the patient’s individualized risk factors and recommended interventions. Through the collaboration with the transitional care team, the patient was able to receive proper education on daily weigh-ins, focus on disease specific nutrition, schedule follow up appointments with appropriate providers, and involve his caregiver in the patient’s plan of care using teach-back methods to ensure understanding of instructions.

Within the last 30 days, this patient has not returned to the hospital for a readmission due to exacerbation of his condition.
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DVT: Progression Prevention
By identifying the socioeconomic drivers for this 54-year old man’s readmission and the best interventions based on his individualized needs, the care team stopped the progression of the man’s deep vein thrombosis to a pulmonary embolism.
A 54 year old man was discharged from the hospital diagnosed with Deep Vein Thrombosis (DVT) and placed on an anticoagulant. At admission, the man was flagged as a high risk for readmission due to multiple diverse factors but, especially relevant were two socioeconomic factors – poor transportation and low household income. One of the top recommended interventions for this patient was a follow-up visit to his PCP following discharge. However, three days later, the patient arrived late to his follow-up appointment. While discussing the plan of care with the patient, the physician learned that the patient had arrived by taxi and did not fill his Eliquis prescription because he couldn’t afford his medication. The physician confirmed progression of the DVT and the risk of progression to a pulmonary embolism. Noting the socioeconomic risk factors, the physician enrolled the patient in a Drug Assistance Program. The anticoagulant prescription was started, and this patient avoided a potentially severe readmission.
If the Jvion insights were taken into account and the individualized recommended interventions were actioned upon by case management before the initial discharge, this patient would have had the resources available to fill his prescription and transportation could have been arranged for his follow up appointment. The physician on this case mentioned that this was a “near miss” and he was fortunate that he did make his follow-up appointment via taxi, where other patients would not have been as fortunate.
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