It probably comes as no surprise that primary care physicians, who know their patients best, can accurately predict the likelihood those patients will end up in the hospital within the next year.
Primary care physicians (PCPs) were as accurate as commonly used predictive algorithms in identifying patients at high risk for hospitalization, according to a study published in the American Journal of Managed Care.
Identifying patients at increased risk for hospitalization is key to designing interventions to prevent avoidable admissions. Primary care doctors, who know the clinical and psychosocial needs of patients, can help identify factors missed by predictive algorithms, the study found.
Researchers asked primary care doctors managing a panel of at least 100 patients to review a list of randomly selected patients. They were asked: “Would you be surprised if this patient was admitted to the hospital in the next year?” Patients were categorized as high or low admission risk based on the doctors’ answers. Of 9,594 patients, doctors designated 21.2% as high admission risk and 78.6% as low risk.
When compared with commonly used risk stratification instruments, doctors did just as well at predicting which patients would need future hospitalization.
“Given the predictive accuracy of PCPs’ clinical assessment, efforts to identify patients at high risk for future hospitalization should aim to incorporate the unique insight that PCPs have about predisposing biopsychosocial factors,” the researchers concluded.
And the better PCPs know patients, the more they can reduce hospital admissions and lower costs. Greater continuity in primary care makes a difference, according to a previous study, which found patients who met with the same doctor most frequently had about 12% fewer admissions compared to those with lower continuity of care.