By Ken Perez, vice president of healthcare policy and government affairs, Omnicell, Inc.
During the 2020 presidential election campaign, the top dozen or so health policies advocated by the Biden-Sanders Unity Task Force Recommendations, the Democratic Party Platform, and the Joe Biden for President Campaign Website fell into two distinct categories: ambitious progressive policies that would probably require a “go-it-alone” approach by the Democrats; and more moderate bipartisan policies that could be passed under the current rules in the Senate as an outcome of traditional political compromise.
Pursuit of the former approach is fashionable, as many Senate Democrats have advocated elimination of the filibuster. In addition, Senate Parliamentarian Elizabeth MacDonough recently determined that Democrats may be able to employ a fast-track process known as budget reconciliation multiple times before next year’s midterm elections, potentially allowing them to pass a bill with a simple majority, assuming that all 50 Democrats fall in line and Vice President Kamala Harris casts the tie-breaking vote as president of the Senate.
Nevertheless, there are key players in the Senate dedicated to pursuing bipartisanship.
However, the tools that allow us to do extraordinary things contribute to nearly all of the problems physicians and their practices face in healthcare. IT is to blame for healthcare’s problems; not lack of payment reform, overarching government intrusion, lack of research, the fact that doctors are only able to spend about eight minutes with each patient per visit, etc.
Guest post by: Sai Subramaniam, Ph.D., Business Head, Life Sciences & Healthcare at Persistent Systems
According to a recent report only 16 percent of hospitals have clinical decision support capabilities, but IT leaders call it a top priority for the next 12 months. Healthcare reform is all about achieving better quality care at lower costs, and clinical analytics is integral in delivering on this promise. For example, reducing 30-day r-eadmissions and hospital-acquired infections alone is expected to save more than $25 billion dollars in the healthcare system. Analytics on integrated claims and clinical data will allow health systems to pinpoint effective clinical and operational interventions. Here are five high-impact outcomes that health systems can achieve using clinical analytics.
30-day Re-admission Avoidance: Hospital re-admission rates are high for patients whether they are in Medicare, Medicaid or Private insurance plans. People with multiple chronic conditions and mental health conditions are at an increased risk of re-hospitalization because of inadequate care at discharge. Demographic and social factors also dictate if the care transition will be effective or not. Evidence-based rules allow stratification of patients based on these factors. This allows caregivers to give more attention to high-risk patients during hospital discharge.
Enhanced Surveillance and Preventive Care: Growing evidence suggests that education and health coaching will facilitate behavior change and achieve cost savings. The population in the program needs to be screened and stratified to identify at-risk patients. Predictive modeling and business rules can help to identify individuals who may not be diagnosed but have relatively high risk of developing diabetes in the future. Similarly, a cancer surveillance model based on linking environmental, genetic, and lifestyle factors can be used. This will allow early interventions and proactive follow-up care.
Improved Medication Adherence: Non-adherence is said to be responsible for more than 10 percent of hospital admissions and 40 percent of nursing home admissions. Patients on average don’t fill more than 25 percent of new prescriptions. Costs because of lack of medication adherence exceeds $100 billion. Predictive analytics on patients’ past prescription claims data will allow the health system to create an adherence score, and facilitate a proactive approach to managing compliance.
Unplanned Admission Avoidance: It’s important for health systems to identify patients with chronic conditions who may be at risk of emergency hospitalizations. For example, studies suggest that people with respiratory and cardiac comorbidities, with higher hospital utilization in prior years, have a higher probability of hospital admission. Determination of such factors along with socio-demographic characteristics, will allow application of predictive models to identify people at-risk.
Length of Stay Performance Management: Several factors impact the patient’s length of stay in the hospital. This includes demographic as well as hospital operational characteristics. There are standards for length of stay based on diagnosis related group and clinical disease factors. By comparing this with patient profiles, providers can utilize resources efficiently to provide optimal patient care. This will result in significant cost savings as better case management should help to reduce the average length of stay.
Dr. Sai Subramaniam is the Vice-President of Persistent Systems’ Life Sciences & Healthcare business. In this role, Sai is responsible for the overall business growth of Healthcare & Life Sciences business segments.
Healthcare reform was ignited by ARRA, which became the catalyst for much of the changes currently taking place in the health IT landscape, and though meaningful use is profoundly changing the way data is collected, according to some we’re a very long way away from actually being able to do something specific and positive with it.
Everyone in the healthcare community is focusing on regulation and meeting the mandates of the reform, from a healthcare technology perspective. Things get a little lopsided when the discussion turns to how the information gathered in meaningful use relates to clinical outcomes.
According to Dr. Akram Boutrous, who leads the consultancy BusinessFirst Healthcare Solutions, right now there is simply no way of collecting all of the data available in the healthcare community on a global level.
As far as he and others are concerned, under the current healthcare reform model there’s too much attention being placed on healthcare technology, including electronic health records, when there is still a mighty void between the tools used to gather the data and the tools (which don’t yet exist, he says) used to analyze the data.
“There are still many tools required to predict what is most likely going to happen in a given scenario and the best course of action to take,” Boutrous said.
He describes the current health IT landscape like an iPad without apps to use on it. “You can look at it, but you can’t do anything with it.”
This means we’re back where we have always been – in a land of silos where the information they contain stays contained without any real chance of it going anywhere to do any good.
Without interoperable systems that can communicate on a much larger scale, there’s certainly no room for even discussing the advancement of the ACO concept. “I’m pessimistic that ACOs as defined [in health IT] will provide meaningful change in healthcare,” he said.
The catalyst for change, he thinks, is the payer community and non-government organizations. Even though the federal government set the foundation for health reform, it won’t be able to maintain a successful program, and innovation will fall by the wayside.
“The non-government side of the world has taken the bull by the horns and made some very innovative advancements,” he said, while the public sector sought clarification of the reform mandates through court and legislative actions.
Until better tools can be implemented and adopted, and a culture change embraced, we’re simply not going to see models like ACOs develop according to the timeline many industry “experts” claim.
Until there are actual tools that provide meaningful support to the community and allow for some sort of global analyzing of specific populations and data sets in real time, healthcare will remain a production-based market where accountable care remains nothing more than an idea.
The market needs more than static components and databases, and health IT needs to evolve and incorporate more capabilities to that make possible, and engage in information exchange before we can begin to move to an accountable care model.