Wound Infection Risk Factors Identified Early

Non-healing wounds are a prevalent concern in skilled nursing facilities, especially among patients with diabetes. Fortunately, with SAIVA’s artificial intelligence tool, caregivers can know which patients are at risk and act proactively. This article considers an example where 65-year-old James’s wound infection risk factors were detected early.

According to the American Journal of Managed Care, “conservative estimates put the prevalence of Medicare beneficiaries who are impacted by wounds at about 15% (8.2 million), with diabetic infections comprising the second largest category.” At the same time, “one of the most harrowing consequences of non-healing wounds–particularly diabetic foot ulcers (DFUs)–is that they can become so complex, with concomitant depth, infection, and ischemia that the only recourse is amputation.”

Rehospitalization for amputation takes a toll on the patient’s body and mind. The individual must adapt to mobility limitations and is at higher risk of depression and social isolation. In fact, post-amputation mortality rates run 27.3% within one year, and 63.2% within five years following amputation.

But, what if the care team could know in advance that a patient is at risk of rehospitalization? That’s what SAIVA healthcare’s artificial intelligence tool offers the clinical team. With artificial intelligence-driven machine learning analyzing the electronic health records (EHR) for that particular facility, the tool provides daily reports identifying would infection risk factors and offers insights to prompt focused action and preventative care.

For nurses, the most exciting part of SAIVA’s system is that it works with the documentation that is already taking place, thus no additional documentation requirements (UDAs or Progress Notes) are needed for the system to make predictions.

SAIVA Healthcare in Action

Take the case of James. The 65-year-old was admitted to his facility after being treated in hospital for sepsis related to a wound infection and osteomyelitis. James also has diabetes, chronic obstructive pulmonary disease, pressure injuries, and sepsis comorbidities.

With SAIVA’s cutting-edge technology analyzing the medical data available, changes in James’s condition trigger his appearance on the daily risk report delivered by email to the care team. Not only listing his name, the risk report identifies particular wound infection risk factors that are collected from different parts of the EHR to aid in decision making. For example:

  • Elevated blood sugar value of 225
  • Severe pain
  • Changes in respiratory rate
  • Patient refusal of pain medication
  • Change in wound status
  • Increase in temp

Taking all this into account, the nurse can start a SBAR and work with James’s physicians to proactively request a wound culture and recommend an antimicrobial foam dressing daily until the culture results are received.

The human healthcare team could track these indicators and identify a worrisome trend; however, there isn’t enough time given the staffing challenges to comb through the different sections of the EHR and connect the dots. Instead of relying on overworked staff to put the pieces together, SAIVA’s AI tool detects subtle changes, sends a report to the care team, and encourages early interventions.

Now the facility can treat James in place and avoid the added trauma of another hospital visit. SAIVA supports care delivery and expands access to evidence-based treatment approaches with its support of beleaguered patients and providers.

SAIVA Saves with AI-Supported Care

With so many factors to consider, detecting and preventing clinical decline is a formidable challenge for the clinical team. Learn more about how SAIVA dramatically improve care quality and operational performance nationwide. Request a demo today!


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  • Become aware of the clinical decline within your patient population