By Andrea Sorensen, associate vice president of product consulting, MedeAnalytics.
Healthcare providers on the front lines of the coronavirus pandemic continue to be overwhelmed by the increase of cases worldwide. Physicians, nurses and other direct providers are overworked, tired and mentally exhausted from non-stop diagnosis and treatment during the pandemic. And just as the number of new cases seems to decrease, they rise again.
In the US alone there are more than 10.3 million cases and 241,000 deaths. Worldwide, cases number more than 51.8 million with more than 1.2 million deaths. These numbers, undoubtedly, will continue to grow in the coming months. “By June 2020, the COVID-19 pandemic had caused hundreds of thousands of deaths around the world, triggered the largest quarterly contraction of global GDP ever recorded, and left hundreds of millions of people without jobs,” according to research published by the McKinsey Global Institute.
Physicians, nurses and other healthcare providers are not immune from the coronavirus. From its deadly effect or the mental health impact of dealing with the pandemic each day. To date, more than 922 healthcare works in the US likely have died following contact with patients. “America’s health care workers are dying. In some states, medical personnel account for as many as 20% of known coronavirus cases. They tend to patients in hospitals, treating them, serving them food and cleaning their rooms,” according to KHN and The Guardian.
Overall healthcare providers, like those of us in society in general, are extremely stressed by the coronavirus pandemic. A study published in Psychiatry Research found “(o)f all 442 participants, 286 (64.7%) had symptoms of depression, 224 (51.6%) anxiety, and 182 (41.2%) stress. Being female, young, and single, having less work experience, working in frontline were associated with higher scores, whereas having a child was associated with lower scores in each subscale.”
But statistics aren’t necessary to understand that healthcare providers will continue to face substantial anxiety and rising tension for the foreseeable future. “Health-care providers were challenged by working in a totally new context, exhaustion due to heavy workloads and protective gear, the fear of becoming infected and infecting others, feeling powerless to handle patients’ conditions, and managing relationships in this stressful situation,” The Lancet reports.
By Abhinav Shashank, co-founder and president, Innovaccer.
Consider a situation where healthcare is not just an industry term — a situation where EHRs are not an integral part of physicians schedule but just a support to providing care. All considered, imagine a situation where patient-centric care actually involves the patient, and patient engagement is not just a buzzword but a reality. Unfortunately, all these imaginations were supposed to be a reality, but still, healthcare managers and organizations are struggling with the problems such as the lack of patients’ adherence to medication, varying trends in the population health, and a lot more. Patient population, nowadays, expect the same on-demand delivery convenience from the healthcare organizations as they get from the other companies, like Netflix.
Why is Patient Engagement the Core of Providing Patient-centric Care?
To understand the value of the patient in the entire care continuum, let us take an example. Consider a patient, Marcus, who works at an IT firm and is affiliated to a Commercial ACO in his county. Marcus is a 65-year-old male suffering from comorbidities like Type 1 Diabetes, and diabetic retinopathy. He is at constant risk of sporadic elevated blood pressure.
In the year 2016, Marcus visited the ED approximately five times. Considering the situation, his primary care physician referred him to a specialist and prepared a schedule comprising at least two monthly visits.
The year 2017 started with a lot of workload for him at his firm, and he was unable to keep up with the prescribed schedule. Because of improper communication between his PCP and him, his physician was not able to keep track of Marcus’s health. As a result, the ED utilization rate for Marcus increased from five times to nine times. Because of enhanced stress and improper quality of care, the sporadic episodes of elevated blood pressure turned into a constant problem of hypertension. Also, the overall cost of care for Marcus increased drastically.
Challenges in Achieving True Patient Engagement
Patient engagement, in itself, is not as simple as ABC. It is not just bringing patients in the cycle of care continuum but enhancing the patient’s skills, ability, knowledge, and most importantly, willingness to participate in the task of managing his own care. The concept of providing care with “engaged patients” sounds great theoretically, but it is not that smooth sailing. According to a survey, nearly 87 percent of the patient population believes that communication with their doctor apart from their scheduled appointments is really important.
The major flaw is the lack of awareness among the patients regarding their care procedures. Many patients are ignorant of the clinical processes which a physician follows, and they might miss out on major health details. With no actual knowledge of the disease symptoms, patients might not report to their physicians which might lead to reduced patient engagement, not to mention the increased risk of developing a chronic disease.
Care teams play a vital role in engaging the patients through regular follow-ups. Irregular and fragmented workflows of care managers and lack of personalization might lead to the generation of ineffective care plans for the patients. Reduction in patient engagement could also be the result of under utilization of technologies to analyze the massive amount of patient data that care managers have at their disposal. Through building more personalized care plans, patients can be engaged at a more grass-roots level.
Driving Effective Engagements through Value-based Care
Predictive population health analytics is the answer to nearly every problem linked with patient engagement. Advanced predictive analytics tools will help in dealing with the problems of disparate data systems and can pinpoint the exact area on which healthcare organizations can focus. Leveraging the insights obtained by data analytics, care teams can prepare the statistical models to prioritize each patient and can take necessary measures to engage patients in the process of decision-making. Understanding the patients’ habits by the care teams increases the chances of preparing personalized care plans for them and enhancing the level of patient satisfaction.
Vyasa Analytics provides a highly scalable deep learning platform for organizational data, enabling conceptual querying and collaborative analytics to help inform key decisions derived from your most valuable information assets.
Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations
Dr. Christopher Bouton earned his Ph.D. in molecular neurobiology from Johns Hopkins University and sold his first big data software company, Entagen, to Thompson Reuters in 2013. Living in India for four years as a boy, he developed a great respect for Vyasa – an important Hindu figure, storyteller and compiler of information – and believes that AI approaches will help us better compile and gain insights from our data systems. In 2016, he founded Vyasa Analytics to apply AI in life sciences and healthcare.
Vyasa engages with life sciences and healthcare organizations to educate the industry about deep learning technologies, including speaking alongside executives at conferences and events. Dr. Bouton is also a frequent contributor and commentator to industry publications.
In 2016, the pharmaceutical industry spent some $157 billion on research and development. This figure is set to increase to more than $180 billion by 2022. The healthcare analytics market was $8.69 billion by 2016 and is estimated to reach $33.38 billion by 2022.
Vyasa is positioned to capture hundreds of millions of dollars in these markets by allowing organizations to conduct analytics on data relevant to their research. Other analytics companies in the space experiencing rapid growth include Lattice.io (acquired by Apple for $200 million), BenevolentAI (valued at $1.7 billion) and Exscientia (recent deals with GSK for $43 million and Sanofi for $273 million).
Who are your competitors?
While there are many deep learning companies, Vyasa is the only one applying deep learning to life sciences and healthcare specifically.
How your company differentiates itself from the competition?
Focusing in the life sciences and healthcare verticals is a key differentiator for Vyasa. In partnership with life sciences and healthcare organizations, we build software to help design better therapeutics, free up researchers for higher-level thinking and solve problems that matter for humanity.
Business model: Vyasa has a B2B business model. Every project is a blend of software licensing and services, provided to the life sciences or healthcare organization to advance their research goals. We are projecting upwards of $3 million in revenue in 2018.
As a provider, you probably have been living with meaningful use in the last many years, and now, MACRA (Medicare Access and CHIP Reauthorization Act), which combines parts of the Physician Quality Reporting System (PQRS), Value-based Payment Modifier (VBM), and the Medicare electronic health record incentive program into the Merit-based Incentive Payment System, or MIPS.
What really is the part of MIPS that matters, for this year and next, anyway? 2017 is the transition year of MACRA, but you need to report something (for various measures) or lose 4 percent Medicare payment adjustment in 2019. If you make a partial-year (90 consecutive days) report by October 1, depending on how you fare against the CMS’ annual performance benchmark, there may even be a chance to get a positive Medicare payment adjustment. In general, a provider will report in the four MIPS performance categories: quality (weighted 60 percent of total in 2017), cost (not weighted in 2017), improvement activities (loosely “care coordination,” 15 percent ), and Advancing Care Information (“EHR use”, 25 percent). Then in 2018 and 2019, with improvement activities and advancing care information remain the same, the quality category will be weighted 50 percent and 30 percent respectively, giving way to cost (10 percent and 30 percent in each of 2018 and 2019).
This sounds like high school all over again – the authority sets the goals that arguably lead you to learn the materials that matter, and grade you on them. If you score well in the four MIPS performance categories, chances are your operations are running quite well. But deep down, perhaps your priorities are simply to provide great patient care, and get compensated for your expertise and services. Then this high-school approach of grading your services, and you – yes, your performance score will be available publicly on the Physician Compare website – becomes a distraction that few providers like to deal with.
So how will you live with this reality? One approach is to actually embrace and integrate MIPS into your operations! Then all MIPS requirements don’t just become some checkbox items you try to complete, but actually a tool to improve your operations. Here are three ways to “take advantage” of MIPS as a guideline to help you thrive:
Embrace a Data-driven Approach
Run your operations based on data. Many EHRs provide at least some basic level of reports that allow you to keep a finger on the pulse of your operations. Make the relevant reports accessible to your team. For the metrics that are relevant to your operations, dedicate a periodic review session to keep everyone abreast of the numbers, and your targets. To leverage MIPS to improve your bottom line, you will want at least some level of visibility through these reports how working those numbers will bring more revenues and/or patient satisfaction, or lower cost. Then it will become clear MIPS can benefit your operations.
Integrate MIPS Efforts Into Your Workflow
Then the team is to identify and make sure they engage the patients that fall in the categories of the reporting metrics to complete the required actions. While in a smaller clinic, some way of patient tracking; e.g. shared call list, may work fine. If your targets involve hundreds or even thousands of patients over a period of time, an automated, smart workflow approach will serve the situation much better. The smart workflow approach is part of the turnkey service my team at LucidAct built after experiencing such patient-care collaboration problems at San Francisco General Hospital in a consulting engagement. Smart workflows keep track of what have been done by whom for a patient, and conditionally activates the next task(s). It can also automate tasks such as calling a patient. Such care-action details in conjunction with the reports above will reveal how the team’s efforts chisel (or not) off the workloads, and improve the bottom line. Having them available in the review sessions ties the effectiveness of the team’s efforts back to the MIPS targets, allowing you to make adjustments to your operations as needed.
Guest post by Steve Tolle, chief strategy officer, Merge Healthcare, an IBM Company.
The volume of health-related data available to physicians and other healthcare providers from disparate sources is staggering and continues to grow. In fact, a 2014 University of Iowa, Carver College of Medicine report projects that the availability of medical data will double every 73 days by 2020. Such data overload can make it difficult for clinicians to keep up with best practices and innovations.
Perhaps because imaging is so pervasive in healthcare, the medical imaging field has turned to data analytics and cognitive computing to help clinicians use large volumes of data in a meaningful way. These decision-support tools help them manage data to improve patient care and deliver value to referring physicians and payers.
At RSNA15, the crowds packed presentations on data analytics and cognitive computing and flocked to vendor exhibits featuring these decision-support tools — indicators of their expanding role in healthcare. In years past, exhibit space was primarily devoted to showcasing new imaging modalities.
Interest in analytics is growing rapidly as the U.S. health system transitions from volume- to value-based payment models — models that challenge physicians involved in medical imaging to demonstrate value. Physicians are under pressure to deliver educated, accurate, useful and efficient interpretations even as imaging studies become increasingly large in size and complex in scope. And these physicians are expected to communicate this information quickly and in a user-friendly manner. As a result, clinicians are turning to analytics-based solutions to boost efficiency and enhance the quality of their service to help them deliver the value demanded by payers, referring physicians and patients.
Guest post by Michael Simpson is the CEO of Caradigm.
It’s been five years since the HITECH Act was enacted as part of ARRA, and while there’s still a lot of debate about the technical details, rules and timelines involved with electronic health record (EHR) adoption and meaningful use, it’s clear that the focus on EHRs – and incenting hospitals and professionals to use EHRs in a meaningful way – represents a critical, foundational step in transforming health care in this country.
After all, meaningful use targets the right goals – goals that every hospital, health system and healthcare professional supports, including improved quality, safety and efficiency of care; reduced disparities; more engaged patients and families as core members of the care team; improved care coordination and population health; and more secure patient health information.
More important, the stages of meaningful use drive a set of progressively more advanced capabilities that are fundamental to achieving those goals. Digitizing data was the first critical step, and the good news is that according to a recent HHS press release, about 60 percent of all hospitals have adopted an advanced EHR, leaving the paper world behind. The next steps are sharing that data – securely – among providers and patients, reporting on quality to understand and improve it, using clinical decision support at the point of care, and many other capabilities critical to transforming care and outcomes. If providers and professionals meet meaningful use requirements, we should see more transparency, greater efficiency, reduced waste and more healthy people in our communities over time.
Stage 2 Challenges
It’s a long and challenging journey, and while hospitals and health systems are making good progress against Stage 1 requirements, very few are prepared for Stage 2. In fact, according to survey data from the American Hospital Association, fewer than 6 percent of hospitals have met the criteria for Stage 2, and only 10 percent have met the requirement for patients to be able to view, download and transmit their health information online.
Why are providers getting stuck as they try to move to Stage 2? Because as the requirements become more demanding – e.g., using clinical decision support, generating patient lists, protecting patient health information, engaging patients – these organizations need a new set of technology capabilities to meet those requirements. These capabilities leverage and extend the functionality and benefits of the EHR.
Moreover, to reach the ultimate goals targeted by Meaningful Use — improved quality, efficiency, outcomes and population health — providers will need to aim even higher than meeting the requirements of meaningful use stages, strategically using data from EHRs and myriad other systems across the care continuum to enable a new level of capabilities.
Guest post by Nilesh Chandra and Nick Mathisen, healthcare experts at PA Consulting.
Healthcare as an industry is undergoing rapid, fundamental changes brought about by reform. The Affordable Care Act of 2010 turned the incentive system upside down for healthcare providers, moving them from fee-for-service payments to Accountable Care Models. Providers who previously made money by separately charging for each procedure and bore little financial risk for patient health, now get paid a single bundled amount for providing care for a group of people, with incentives to reduce the total cost of care and share in those savings. Taking a cue from Medicare and Medicaid, private health insurers are increasingly adopting similar payment models.
The challenges today
Doctors and nurses who had the responsibility to help sick people get better, are now expected to keep people healthy. Hospital administrators who were measured on financial metrics like bed utilization are now expected to keep people out of hospitals. Traditional healthcare involved dealing with sick people who came in to hospitals and clinics. Tomorrow, healthcare will be about proactively engaging with healthy people and encouraging them to adopt behaviors that keep them healthy. This will involve outreach and engagement in entirely new ways that the modern healthcare industry has not done before.
The future of healthcare
The future of healthcare is outside the boundaries of what our modern healthcare industry knows how to do.
Think about it. Many industries are facing disruptive innovation where the future of the industry is completely different from what has been the norm. For example, the PC industry with the rapid shift to tablets, or retail with the increasing move to online channels. However, both of those industries have always been subject to rapid innovation and players have learned to evolve rapidly. The transformation in healthcare is more profound because it is larger in scale and it has a much greater impact on people’s lives.
So what does the future of healthcare involve and how can technology help? There are three key elements that the healthcare industry has to learn to be more efficient and proactive:
Caring for the chronically sick more efficiently with wearables
The rate of diabetes, heart conditions, obesity and other chronic conditions are projected to continually rise. The chronically ill consume a large proportion of healthcare, therefore any efficiency gained in providing care for them translates into significant savings in the overall health system. A recent study from Robert Wood Johnson University hospital found that 80 percent of all heart-attacks could have been prevented by simple changes in lifestyle. Changes in lifestyle will have a similar positive impact on other chronic conditions as well.
Electronic health record (EHR) technology has become truly transformative for the healthcare industry; prepared or not, healthcare teams are increasingly relying on new information technologies to improve the delivery and management of care. EHRs have enabled faster and easier access to patient information, and hold the promises of improved workflows, efficient sharing of information across communities and reduced costs for many physicians and hospitals.
But now that nearly 80 percent of physician practices in the U.S. today have EHR systems in place and the Centers for Medicare & Medicaid Services’ (CMS) meaningful use program is well underway, it is time to look to the next stage of health care technology and innovation. Health care teams must now move beyond the first step of digitizing patient records to transforming this valuable data into meaningful and actionable knowledge that will help care teams make more informed decisions at the point of care and ultimately, improve outcomes.
For this impact to take place at both the individual level and at the population level, care teams need to leverage clinical analytics that will provide visibility into important clinical trends across the entire population. For example, being able to review trends in diabetes care or readmission rates across a population represents an opportunity for specific, meaningful change to improve care delivery and outcomes.
For a practicing clinician, “population health management” means being able to see where an individual patient is within the clinician’s or clinic’s population (e.g., whether the individual’s chronic condition is above or below population benchmarks) and to take action at the point of care, as well as being able to refer to relevant population health metrics.
For a patient, clinical analytics presumes trust, not only in the competency and care of the physician, but also in the security of his or her information. Population health management and analytics tools must ensure that patient information can be gathered, stored, and used in a way that is demonstrably secure.
Care teams should consider four key elements when exploring clinical analytics tools for population health management: