Data For Improving Healthcare vs Data For Exasperating Healthcare Workers


The phrase “healthcare data” either strikes fear and loathing, or provides understanding and resolve in the minds of administration, clinicians, and nurses everywhere. Which emotion it brings out depends on how the data will be used. Data employed as a weapon for purposes of accountability generates fear. Data used as a teaching instrument for learning inspires trust and confidence.

Not all data for accountability is bad. Data used for prescriptive analytics within a security framework, for example, is necessary to reduce or eliminate fraud and abuse. And data for improvement isn’t without its own faults, such as the tendency to perfect it to the point of inefficiency. But the general culture of collecting data to hold people accountable is counterproductive, while collecting data for learning leads to continuous improvement.

This isn’t a matter of eliminating what some may consider to be bad metrics. It’s a matter of shifting the focus away from using metrics for accountability and toward using them for learning so your hospital can start to collect data for improving healthcare.

Digital Health, Health Reform & the Underserved – Where Will 2017 Lead?



In these first days of the Trump Administration, there is a great deal of uncertainty, but it’s clear that healthcare will remain in the spotlight. Repealing and replacing “Obamacare” is still at the top of the Republican party’s—and President Trump’s—agenda.

Congress and Trump have already taken steps to repeal the Affordable Care Act (ACA), though a replacement for it has yet to be articulated. Trump promises “insurance for everybody” in a form that is “much less expensive and much better,” but has yet to reveal details about how to meet his goals.

While changes in healthcare policy will have ramifications for all Americans, members of underserved populations are likely to be disproportionately impacted because they are statistically less healthy  and are also the least likely to have health insurance coverage. Parts of the ACA address Medicaid, which provides health insurance to 70 million people—by definition among the poorest Americans. Nine million whites make up the largest racial group of people who have gained coverage as a direct result of the ACA, but significant numbers of minorities, including 3 million African Americans and 4 million Hispanics, have also gained coverage. The ACA also helps LGBT Americans by forbidding discrimination due to gender or sexual orientation, and by enabling same-sex families to apply for joint healthcare coverage. According to a report issued by the nonpartisan Congressional Budget Office on January 17th, if the ACA were to be rolled back without a replacement, 18 million people would lose health insurance in the first year. There would also be significant restrictions in reproductive health services for women.

What 32 Million Tweets Tell Us About Health & the Twitterverse


How can we gauge whether America is prioritizing health and well-being? Since public attitudes toward health-related topics are widely shared on social media, we gazed into the mirror that is Twitter and tried to answer that question by sifting through 32 million health-related tweets, one of the largest social media samples ever collected for health research.

Posts and conversations on Twitter have the potential to shed light on the public’s views about a seemingly endless array of health-related topics—obesity, exercise and fitness, safe sex, alcohol use, medication adherence and mental health. Accordingly, researchers have turned to social media to better understand these topics.

JP Morgan Week: Lessons For Investors From the Theranos Story



Theranos raised $900 million from investors and achieved a market capitalization of nearly $9 billion. Today, its investors may have lost most of their money and the company is pursuing a new strategy. It’s a familiar story to lenders and investors and likely to be hallway chatter today as the 35th Annual J. P. Morgan Healthcare Conference convenes in San Francisco.

Theranos targeted the lucrative blood testing market offering a new technology that allowed labs to do 30 blood tests almost instantly with a single drop of blood. The company began its operations in 2003 with a $5.8 million investment from Draper, Fisher, Jurvetson and other venture funds. By 2010, it had raised $83.4 million more in three follow-on rounds and then scored a reported $633 million investment in 2014 increasing its market value to $9 billion. In those 11 years, the company operated in relative secrecy: its 60-plus patent filings gave clues about its activities while its CEO, Stanford drop-out Elizabeth Holmes, shunned the spotlight.

Improving MACRA’s Chances of Success


Many providers view the Medicare Access and CHIP Reauthorization Act of 2016 (MACRA) with skepticism. MACRA represents the largest implementation of physician pay-for-performance ever attempted in the United States. Starting in 2019, MACRA will integrate and potentially simplify performance measurement by combining a number of measures and programs. It will also increase the magnitude of financial rewards and penalties, which could help motivate practice change for the better.

One of the more controversial aspects of MACRA is its Merit-Based Incentive Payment System (MIPS) for physicians and practices not participating in alternative payment models. One physician captured the prevalent skepticism when he wrote in the public comments on MACRA: “This rule will wreak havoc with my practice while offering absolutely no evidence that it will do anything to improve patient care.” Partly due to the many public comments, the Center for Medicare and Medicaid Services (CMS) has made substantial changes to the final rule. However, there is room for further changes during the rollout – and potentially strong interest in doing so from Tom Price, the physician nominated to lead the Department of Health and Human Services.

Star Wars Is Really About Protecting Patient Data (Yes It Is)


Star Wars may be a light-hearted adventure film series at its core, but that hasn’t stopped professionals and academics from extracting some real-world lessons from the series. A couple of prominent examples include a thesis on the economic impact of building the Death Star and NPR’s political science analysis of the inner workings of the galactic senate.

With the latest Star Wars film, Rogue One, it’s the healthcare IT industry’s turn to take a crack at the known universe’s most popular space saga.  Be forewarned: the following analysis includes spoilers from the new film.

A key component the plot is that the Empire suffers a series of data breaches that have a catastrophic impact on the organization. The connection to the healthcare industry should already be clear. Even with improving safeguards, over 11 million individuals were affected by healthcare data breaches perpetrated by cyber-attacks in 2016. We can learn from the Empire’s mistakes by looking at the film’s three most prominently featured security measures, and how a real-world organization can do better than Darth Vader when it comes to protecting sensitive information.

Internet Self-Diagnosis: Mapping the Information Seeking Process


We’ve all been there. It’s early morning, and you wake up feeling groggier than usual, sensing the onset of a sore throat and a runny nose. Before crawling out of bed, you grab your smart phone and, naturally, Google “groggy sore throat runny nose symptoms.” Hundreds of results pop up, suggesting various illnesses and links to seemingly promising remedies. How could anyone filter through page after page of links, ranging from everyday allergies to deadly diseases?

Many of our health choices are made outside the doctor’s office. The simple decision of whether symptoms are severe enough to warrant visiting a healthcare provider is one of them. For some patients, that decision is easy, because regardless of the severity of symptoms, from a simple cough to leg pain, getting in to see a healthcare provider is easy. Unfortunately, many people still struggle to find a healthcare provider, get an appointment, and/or obtain transportation. These individuals are left to turn to other health information resources, such as the Internet, to determine whether their symptoms are severe enough to navigate these barriers.

The “digital divide” has become a catchphrase for how differences in educational, social, and economic backgrounds can affect access to web-based tools and services, as well as the general ability to use the Internet.

That divide has serious healthcare consequences: Though the web is not intended to replace traditional medical care, it may offer one of the few available sources of information for those with limited access to health services. While patients who regularly visit a provider are privy to the diagnostic processes of medical professionals, web-based tools may be critical in weighing the severity of symptoms for those with fewer resources and less access. 

The Perils of Precision Medicine


When The White House announced their Precision Medicine Initiative last year, they referred to precision medicine as “a new era of medicine,” signaling a shift in focus from a “one-size-fits-all-approach” to individualized care based on the specific characteristics that distinguish one patient from another. While there continues to be immense excitement about its game-changing impact in terms of early diagnoses and targeting specific treatment options, the advancements in technology, which underlie this approach, may not always yield the best medical results. In some cases, low cost approaches, based on sound clinical judgment, are still the better option. 

For example, tuberculosis (TB) is an infectious disease that continues to pose global burden with 9.6 million new cases and 1.5 million deaths reported in 2014 alone. The large toll is partly due to lack of effective treatments (particularly for drug-resistant cases) but also due to delays in diagnosis. One might think that precision medicine technology leading to improved diagnosis would be effective at minimizing the related death toll but we shouldn’t automatically assume that. It turns out that sometimes the latest technological advancements can be so sensitive that we detect organisms that are not causing disease.

Why the 21st Century Cures Act Is Great News For Healthcare IT


On December 7, 2016, the United States Senate approved the 21st Century Cures Act by an overwhelming margin. Having already passed the House with similarly broad bipartisan support, it now goes to President Obama for signature. Several years in the making, the Cures Act is broad and sweeping legislation that covers many topics, mostly on streamlining and accelerating the discovery of new drugs and medical devices. It includes provisions to improve mental health and substance abuse treatment, and to improve patient access to new therapies, among many other areas covered by the Act.

The Act also includes several provisions that will help accelerate the work of health information technology (HIT) companies and providers working to use healthcare data and information to improve outcomes, reduce variations in care, and better coordinate care delivery. These provisions establish programs and oversight to promote health information interoperability and prohibit information blocking practices.

The Role of Machine Learning in Making EHRs Worth It


Recently, a great op-ed published in The Wall Street Journal called “Turn Off the Computer and Listen to the Patient” brought a critical healthcare issue to the forefront of the national discussion. The physician authors, Caleb Gardner, MD and John Levinson, MD, describe the frustrations physicians experience with poor design, federal incentives, and the “one-size-fits-all rules for medical practice” implemented in today’s electronic medical records (EMRs).

From the start, the counter to any criticism of the EMR was that the collection of digital health data will finally make it possible to discover opportunities to improve the quality of care, prevent error, and steer resources to where they are needed most. This is, after all, the story of nearly every other industry post-digitization.

However, many organizations are learning the hard way that the business intelligence tools that were so successful in helping other industries learn from their quantified and reliable sales, inventory, and finance data can be limited in trying to make sense of healthcare’s unstructured, sparse, and often inaccurate clinical data.

Data warehouses and reporting tools — the foundation for understanding quantified and reliable sales, inventory, and finance data of other industries – are useful for required reporting of process measures for CMS, ACO, AQC, and who knows what mandates are next. However, it should be made clear that these multi-year, multi-million dollar investments are designed to address the concerns of fee-for-service care: what happened, to whom, and when. They will not begin to answer the questions most critical to value-based care: what is likely to happen, to whom, and what should be done about it.