Clinicians depend on timely, accurate data and clear, evidence-based information to deliver safe patient care. But providing care can be a challenge when caseloads are high, clinician burnout and exhaustion are rampant and patient data isn’t always accessible. These challenges can result in issues such as medication errors, hospital acquired infections (HAIs) and blind spots in diagnosis.
Addressing potential patient safety risks with technology
The good news is that technology can support clinicians’ efforts and reduce data barriers with the goal of improving patient safety.
1. Medication errors
Medication errors— 500,000 of which take place daily in the US — are the third leading cause of outpatient visits after cardiovascular disease and cancer. An aging population, a lack of drug indications data and an overburdened healthcare system have increased the risk of prescribing and dosing errors.
Part of the issue is that clinicians need reliable information on a large number of potential drug interactions. Complex factors like comorbidities, pharmacogenomics, lab results, and polypharmacy have clinicians wanting better medication safety screening so they can make clear, decisive decisions. Having embedded drug data solutions to synthesize vast amounts of drug data and providing drug decision support available at the point of care can help clinicians avoid potential errors and treat patients appropriately.
2. Sepsis infections
Hospital-acquired sepsis has a mortality rate of roughly 50 percent. An AI-powered system that identifies early symptoms of sepsis can alert the hospital’s rapid response team and guide nurses through a checklist of recommended steps to diagnose and treat patients quickly.
One 2021 study examined an AI algorithm called SERA that used both quantitative and qualitative data to find and diagnose sepsis cases among patients in Singapore. The algorithm achieved high predictive accuracy 12 hours before the onset of sepsis and increased early detection by 32%. Another study exploring how change management and electronic surveillance solutions can impact outcomes found that these tools reduced sepsis mortality by 53%.
Digital clinical surveillance tools, integrated into the clinician workflow, can aggregate EHR data to alert care teams of sepsis in real time to encourage early interventions while reducing alert fatigue.
3. Incomplete patient records with unstructured data
Having a complete picture of a patient’s health is crucial for delivering proper care. When data is incomplete or hidden in a variety of formats, clinicians may be forced to make decisions without a full understanding of the patient’s history.
While structured data is searchable and usually quantitative, unstructured data comes in all shapes and sizes — notes, images, audio, PDFs, sensor data or video — and is not easily parsed by all programs. Industry experts estimate that about 80% of health data is unstructured, often inaccessible to clinicians and missing from EHRs. Failing to integrate this data locks away potentially helpful insights into individual and population health.
Recent breakthroughs in AI and clinical natural language processing (NLP) make it possible to interpret unstructured health data. NLP that specializes in clinical information and coding can make data available for enhancing patient records and better inform treatment plans.
AI and NLP technology can relieve healthcare systems from having to spend so much time deciphering unstructured patient data manually. For example, a study published in the Journal of the American Medical Association system identified paperwork for quality control and reimbursement as one of the largest sources of waste in healthcare systems. Clinicians and hospital leaders can benefit from unlocking unstructured data for better efficiency, insights on population health trends and individual patient care.
Equipping clinical teams for safer outcomes
Clinical teams and specialists need all the support they can get. By equipping teams with robust technologies that uncover insights hidden in patient histories, support safe medication decision making, provide more optimized and accurate sepsis alerts, clinicians have more information at their disposal to support safer decision making and patient care.