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Personalized patient care: Using data analytics to reduce office visits and improve patient health

Technical Writer

A patient drives one hour across town and waits two more to see a family doctor. Despite the substantial time commitment, the thought of switching to a closer provider never seriously crosses that person's mind; the patient genuinely feels that his or her health matters to the doctor.

Research featured in the Journal of the American Board of Family Medicine shows this kind of personalized patient care can decrease hospitalizations and office visits while improving their overall health. The study found that patients whose doctors provided more patient-centric care by discussing their concerns, analyzing their conditions in light of their broader health record and collaborating to find solutions, had 51.3 percent lower annual healthcare costs than those without.

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1442867971-16-3167/Patient_Blog.jpgA recent KPMG study found that only 10 percent of healthcare providers surveyed are using data to its fullest extent. Your hospital or medical practice already has the tools needed to provide patient-centric care in the form of electronic health records (EHR) for each client. Instead of using this data only to find information related to current issues or visits, you can use data analytics on EHR to detect potential issues and provide proactive treatment measures, which decreases the need for additional office visits. By taking this extra step, your practice can help your patients live long, high-quality lives.

Recommend screenings using predictive analytics

Preventative screenings are typically ordered based on a patient's age or pre-existing conditions. At Stanford University School of Medicine, researchers are using data analytics to determine patients' risk for a genetic disease called familial hypercholesterolemia, Healthcare Informatics reports. This condition goes undiagnosed in 90 percent of patients, often only detected after they have a heart attack. During the study, researchers used predictive analytics to find data patterns that indicated a possible genetic predisposition to this disease and contacted at-risk patients' primary care providers for screening and treatment.

This same practice can be utilized by providers scanning for chronic conditions such as diabetes or heart disease, which may be easily missed because symptoms often seem unrelated until a severe problem appears. According to Forbes, Pfizer also uses this strategy to diagnose fibromyalgia more quickly; patients often spend five years looking for a diagnosis and visiting multiple doctors. By using predictive analytics, providers detect patterns in patients' symptoms and recommend preventative screenings. This approach can prevent life-threatening issues and reduce office visits for reoccurring symptoms.

Identify chronic illnesses and create personalized plans

While the benefits of preventative care are well-documented, HealthITAnalytics.com reports that data analytics helped Ascension Health improve preventive care strategically to reduce hospital visits by 150,000 in a single year. The healthcare provider, which has 1,500 locations in 23 states, flags patients who have been frequently readmitted for preventable conditions, such as acute myocardial infarction, congestive heart failure and pneumonia. Ascension Health then uses care coordinators to create a personalized plan for each of these patients, including medication management and regular primary care visits. Because these conditions are not chronic and are often treated at the hospital, providers often miss opportunities to create a care plan for high-risk illnesses.

Another way to reduce readmissions is to proactively schedule follow-up appointments with patients before they leave the hospital. Accountable care organizations use data to monitor hospital admissions in real-time, creating informative dashboards for medical professionals and pairing patients with the appropriate specialists, Modern Healthcare notes. Providers can proactively monitor inpatient and emergency room visits through data analytics, and then contact the patient via text message to schedule follow-up appointments right before release.

Data analytics helps providers take personalized patient care to the next level by identifying patterns and red flags that are not easily caught by manual processes. By monitoring your patients' EHR, you'll improve their health, demonstrate your concern for their well-being, and keep them driving across town for regular office visits.

Learn more about patient care quality management for healthcare and join us for announcements and knowledge sharing at IBM Insight 2015.