Medication costs make up over 10% of health care dollars and are the fastest growing source of medical expense. Many state and federal officials have called for action that would add cost transparency to the complex, opaque medication supply chain believing that this will lead to lower prices and higher value care. Though this may appeal to our intuition, evidence that medication cost transparency leads to higher value care is scant. In order to add to the evidence base, four other investigators and I embarked on a 9-month investigation designing and evaluating the effect of medication cost transparency decision support built into the electronic health record. Bottom line: it makes a difference. The results were recently published and are available on the JAMIA website: https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocz025/5445905.
Doctors are overwhelmed with data. They spend 12% of their time looking up clinical data when they could be seeing patients and still information gets missed. In fact, the IOM has identified untimely access to clinical data as a leading contributor to the 3rd leading cause of death in the US: medical errors. Existing information systems and electronic medical records are better optimized for billing and documentation than they
are for making care safer. There has to be a better way.
In aggregate, community health centers account for the care of about 20 million people in the US. Over half of these patients represent racial or ethnic minorities and over a fifth (22%) prefer to speak Spanish rather than English.
Most CHC revenue comes from fee-for-service reimbursement paid by Medicaid (40%), private payers (7%), and Medicare (6%). This has led CHCs to pursue many of the strategies for maintaining solvency as other care centers across the US, including increasing patient visit volume and improving operational efficiency.
One problem all clinic sites face is the incidence of no-shows, patients for which an appointment is scheduled but that do not show up. It is estimated that no-shows account for 5-30% of appointments scheduled across the US and it is typically higher at CHCs. No-shows risk failing to deliver appropriate care to patients for whom they are scheduled in a timely or continuous manner, reduce access to scarce healthcare resources for those waiting for appointments, and represent up to 15% of lost revenue for the clinic.
Effective hospital care requires coordination among multiple individuals including therapists, care coordinators, primary teams, consult teams, and nurses. Unfortunately, this coordination is costly to frontline staff often requiring much time and many steps even to identify the appropriate contact. Existing solutions have significant shortcomings without a highly-available best practice.
The 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk introduced a new risk assessment calculator based on aggregate data from several cohorts. According to these guidelines, a patient’s risk according to this algorithm is critical in determining if a cholesterol-lowering statin should be prescribed. Initially, this calculator was available only through a somewhat onerous Excel spreadsheet. Moreover, this was nearly impossible to access through mobile phones, a preferred modality. Continue reading
When people get sick, they have several options for obtaining health care. These include going to the emergency room, urgent care center, or calling a doctor or nurse. However, 80% of people experiencing symptoms start with an Internet search. Unfortunately, searching on Google offers spotty results and frequently leads to undue concern. For example, one is 1000x more likely to encounter “brain tumor” in web search results for “headache” than they are to ever have the disease. Undue concern is a contributor to the 40% of emergency room visits and 70% of physician visits that are considered to be inappropriate.
Every movement we make requires our brains to predict what forces–gravity, an object we’re holding, a strong gust of wind–each of our body parts will experience in order to move in a coordinated fashion. No movement is ever exactly the same and so it is remarkable that we are not constantly tripping over ourselves. It is well-known that humans learn based on previous errors in their movements. My work at the Harvard Neuromotor Control Lab was to investigate how the brain learns to “makes generalizations” about movements and learn from its mistakes. Continue reading