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
I am appreciative for the opportunity to share alongside David some of my journeys in conceiving of and building Symcat during the Johns Hopkins Informatics Grand Rounds. In it, we talk about some of the history of decision support, the technology behind Symcat, and some additional points about entrepreneurship and web development that excite us.
- video of the presentation
You’ve got this great idea for a medical app that will transform health care (or at least a chunk of it).
There is no one path to executing your idea. Particularly for those of us in medicine where the course is clearly delineated (pre-med, med school, residency, etc), acknowledging this fact can be disorienting. My goal here is to suggest one path that has helped me personally get beyond the ideation phase.
Continue reading 5 Steps to Making Your Medical App Idea a Reality at iMedicalApps.
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.