Jvion’s Cognitive Machine Oncology Vectors
30-day mortality risk
Anticipating end-of-life care decisions about the appropriate or preferred treatment can be challenging for healthcare providers and burdensome for patients and their families. The final month of life is demanding clinically and financially with approximately 75% of the total cost of cancer treatment occurring in the final 30 days of life. Understanding the difficulty of end-of-life care, providers are looking to cognitive machine capabilities to ensure that high-quality care is being delivered that is consistent with a patient’s needs, values, and preferences.
The Jvion Oncology 30 Day Mortality vector supports clinical decision-making by predicting the final stages of a patient’s terminal illness with a primary endpoint of early hospice/palliative care. The Cognitive Machine accounts for more than 4,500 clinical, social, and behavioral factors to determine which intervention and care path will improve a patient’s quality of life over the final 30 days. Whether it is symptom management, preparing the family and/or caregivers, or promoting a social support system, these individualized recommendations redirect the plan of care toward the actions that will meet the needs of a patient and his/her family. In addition, these recommendations minimize unnecessary clinical workload, avoid aggressive and unwanted measures of care, and optimize palliative care and/or hospice enrollment based on the patient’s preferences.
30-day readmission risk
Current reported readmission rates for cancer patients discharged from medical services are as high as 27%. Oncology patients may have multiple and complex comorbidities as a result of not only the etiology of their malignancy but also the expected, and thus not completely preventable, complications of treatment. The majority of cancer patient readmissions are due to the acute onset of symptoms, complications of therapy, and progression of disease. Many of these are oftentimes manageable in the ambulatory care setting if the most effective actions that will reduce the risk of a readmission are identified and actioned.
The Jvion Oncology 30-day readmission vector identifies patients that show a high propensity for multiple inpatient readmissions within the next 30 days. The machine empowers clinicians with the patient specific clinical and socioeconomic risk factors driving this risk, which are accounted for in the machine’s recommended interventions. The goal of the vector is to promote targeted interventions in the ambulatory setting the enhance disease outcomes, quality of life, and cost-effective delivery of care while preventing unnecessary readmissions.
6-month deterioration risk
Patients with cancer can deteriorate under various circumstances. Understanding the patient’s functional status can help in determining the best treatment for an individual patient’s quality of life. The current clinical state for measuring how cancer impacts daily living abilities takes into account a five-point scale derived from the Eastern Cooperative Oncology Group’s (ECOG) Scale of Performance Status. But this is a limited approach to identifying a patient’s level of functioning. There are many factors that can predict whether someone is likely to do well or poorly with their disease and not all of those factors may be identified in the clinical setting.
The Jvion Oncology 6 Month Deterioration Vector takes into account the clinical, social, and behavioral risk factors to predict the likelihood of a functional decline. The Cognitive Machine determines a patient’s risk propensity while in the ambulatory setting and prior to the onset of clinical symptoms and/or an acute impact on the patient’s daily activities (e.g. ability to care for themselves, daily activity, physical ability). The individualized recommended interventions direct caregiver attention to not only focus on symptom management but, also to address the existing gaps in the patient’s social environment and cognitive function.
Hospitalizations in patients with cancer are particularly common due to the acute condition and acute symptom onset. Approximately 19% of hospitalizations in patients with gastrointestinal cancer are potentially avoidable and clinicians directly involved in caring for patients with cancer agree that nearly 1 in 4 hospitalizations (23%) are potentially avoidable. Reducing acute hospitalizations is an important strategy for improving the quality, value, and patient-centeredness of cancer care. And optimal outpatient support in the ambulatory setting will better serve these patients in avoiding and inpatient admission.
The Jvion Oncology Avoidable Admissions vector predicts which patients are at risk to be admitted to a hospital within the next 30 days while the patient is still in the ambulatory setting. With this information, care coordinators are able to address individual patient needs and risk factors driving the admission risk by actioning on the individualized recommended interventions delivered by the Jvion machine.
Patient experience and pain management
Pain is among one of the most disturbing and restricting consequences of cancer treatment or care. The incidence of pain amongst cancer patients ranges from 53-64% from an advanced disease stage to all stages of the disease. In addition, two-thirds of cancer patients report that pain interferes with their activities of daily living, and half believe that their healthcare providers do not prioritize the quality of life in their overall plan of care.
The Jvion Oncology Pain Management vector predicts those patients who are at risk for poorly controlled pain as it relates to their condition. Taking into account the comprehensive risk factors associated with pain management – including physical, clinical, social, and behavioral—the care team is able to take specific actions to address a patient’s needs. The machine empowers clinicians with individualized recommended interventions aimed at increasing education on pain assessment and management, facilitating referrals to pain specialists as needed, improving patient adherence to plan of care, minimizing costs associated with ongoing pain management, and reducing unnecessary emergency room visits through the care continuum. Moreover, the care team is able to overcome existing barriers with cancer pain management through the increased communication and collaboration among the multidisciplinary care team and the patient and family members.
Depression is a common co-morbidity of cancer that has a detrimental effect on quality of life, compliance to the plan of care, and additional complications. It has a considerable impact on healthcare utilization and cost, and is associated with substantial suffering. In addition, it often goes undiagnosed or untreated which can have considerable effects on the patient. Prompt recognition of a patient’s risk for depression and effective treatment are critical to address the cancer patient's quality of life.
The Jvion Oncology Depression Risk vector predicts those patients who are at risk for depression within the next six months while the person is still in the ambulatory setting. The oncology depression risk vector takes into account those patients with a previous or current diagnosis with depression as well as those with a propensity toward depression. This enables care coordinators to better understand which patients are most likely to be depressed and which patients are or are not receiving adequate treatment and/or resources to better manage their depression. Based upon the individualized risk factors (clinical and socioeconomic) identified for each patient, actionable recommended interventions are provided to the care team. These individualized recommended interventions enable care coordinators to effectively address individual patient needs, whether clinical or socioeconomic based, to best manage the patient’s plan of care, ensure adherence to medical treatments, and enhance quality of life.
For cancer patients, missing an appointment can have a huge impact on current treatment and the possibility that the cancer will return. In one study published by the International Journal of Radiation Oncology Biology Physics, cancer patients who missed radiation therapy appointments were at a greater than 2x risk of a recurrence. The reasons behind a missed appointment are varied. The financial burden of cancer treatment alone can push people to skip a scheduled doctor visit or not fill a prescription. There are also socioeconomic factors including access to transportation that compound the problem.
The Jvion patient no show vector identifies patients who are at a high risk for missing their appointment and provides advance notification along with the actions that will most likely reduce noncompliance. In addition to identifying those who are at a high risk for a no-show appointment, the machine analyzes each patient at the appointment/encounter level and the visit level (i.e. time and day of appointment) to determine the appropriate recommended intervention. These additional interventions identify patients that could benefit from the following but, are not limited to: telehealth kits, transportation assistance, after hours appointments, and in home visits. Additionally, the machine provides post discharge lists to identify those patients that are likely to no show 48 hours post discharge and again for those at risk to no-show within the next 7-14 days.
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