By Bobby Sherwood, vice president of product development, GuidingCare.
A lack of interoperability permeates U.S healthcare. Despite the rapid adoption of new technologies, we have failed to fully realize some of the most impactful opportunities they present. Data silos that hinder collaboration, efficiency, and innovation stubbornly persist across the industry. For health plans, embracing digital transformation to digitize process and improve member experience pays dividends, but can come with difficult integration and interoperability challenges if not done properly.
There has been a recent spotlight on government initiatives and regulations to address these growing concerns. Take the new CMS proposed rule on interoperability and prior authorization, which will require payers to implement an electronic prior authorization process, shorten the time frames for payers to respond to prior authorization requests, and establish policies to make the prior authorization process more efficient and transparent.
In a world where nearly anything can be instantaneously ordered from your mobile phone or laptop and delivered overnight, it seems inconceivable that prior authorizations – something so critical to member and population health – is managed by an antiquated system. This seamless exchange of data will reduce provider abrasion, improve the member experience and potentially their health outcomes, and ultimately decrease the cost of care, as the manual effort and time linked to prior authorizations markedly decreases.
As we execute on the year ahead, interoperability remains top-of-mind for stakeholders: a new report suggests that barriers such as poor data quality and information sharing remain challenging to over 60% of healthcare executives. For health plans prioritizing interoperability, consider these three areas of focus:
By Susan deCathelineau, senior vice president sales and services, Hyland Healthcare.
Susan deCathelineau
Much like the formation of New Year’s Resolutions, the prediction of technology trends for the coming year has become a tradition among pundits, analysts and vendors alike. As the calendar turned to 2020, Hyland, like many, took the opportunity to look into a crystal ball to predict what the future might hold for the software industry at large, as well as many of the key vertical markets in which it operates.
For example, Hyland leadership revealed six overarching trends for enterprise technology as well as key trends to watch for health IT. At the time, none of us could have foreseen that a global pandemic was coming that would turn all of these predictions on their collective ears.
Of course, the healthcare industry has been particularly impacted by COVID-19. Provider organizations have justifiably focused their attention on responding to the new patient care and staffing needs brought about by the virus. That said, all of the health IT trends Hyland outlined at the beginning of 2020 (interoperability, artificial intelligence and cloud adoption) still have relevance in today’s unprecedented landscape. Although, admittedly, the reasons these topics are trending are for vastly different reasons than we originally anticipated.
I want to revisit these trends under the lens of COVID-19 as well as add a few more to the list in light of current circumstances.
Interoperability
Original insight: Secure access to patient information at any facility throughout a care continuum is an imperative for delivering a longitudinal digital record that travels with the patient. The key is to ensure tight integration between disparate IT systems, and to include unstructured data in the interoperability equation. As much as 80% of essential patient information is in an unstructured format – such as digital photos and videos, or physician notes – and not natively included in an electronic medical record (EMR) system. When removed from a clinician’s view, the patient record is incomplete.
New relevance: Health IT interoperability was important prior to COVID-19, and it’s even more critical now. Providers, patients and public health officials need all-encompassing data in a standardized format to better understand this evolving illness and develop guidelines. The effort to identify risk, control spread and manage the treatment of afflicted patients is a coordinated effort among multiple healthcare providers and external care partners. The easier information can be shared among these varied stakeholders, the better equipped we’ll be to combat the virus.
Artificial Intelligence (AI)
Original insight: Realistic applications of AI are coming into focus in healthcare, showing where the technology will help providers optimize workflows and better analyze the vast amounts of information needed to support improved decision making. Experts view AI technology as complementary and a true asset when it comes to helping physicians analyze the overwhelming amount of patient data they receive daily. Physicians can implement AI to streamline or eliminate tedious tasks, such as manual documentation and data search, or cull information to help them focus on a key area of interest.
The medical imaging space in particular provides a tremendous area for the growth of AI and machine-learning technologies. Clinicians can use them to analyze thousands of anonymized diagnostic patient images to identify and detect indicators of everything from lung cancer to liver disease. These technologies are also being used to accelerate research.
New relevance: AI is being used in a number of ways to address the challenges of COVID-19. For example, AI algorithms have been used to identify the spread of new clusters of unexplained pneumonia cases. Other AI applications are being used to spot signs of COVID-19 infections in chest X-rays and identify patients at high-risk of coronavirus complications based on their pre-existing medical conditions. Still others are scanning the molecular breakdown of the virus itself as well as those of existing drug compounds to identify medications that can potentially target the virus and shorten the span of the illness or lessen the severity of the symptoms. In all of these scenarios, AI is quickly analyzing large segments of data to accelerate research and treatment. This automation is indispensable in an environment where medical staff are stretched to their limits, and the act of saving time could save lives.
By Dr. Chris Hobson, chief medical officer, Orion Health.
Dr. Chris Hobson
Health information exchanges (HIE) help care teams provide more informed patient care by supplying a complete longitudinal healthcare history of the patient to healthcare professionals, as well as enabling high quality reporting and analytics on the data. The goal of an HIE is to accurately store all relevant patient information from as many sources as possible, including medical history, medication history, past treatments and detailed personal information. A comprehensive reporting system allows for health delivery that is more responsive and tailored to each patient, and subsequently, the broader population.
Today the transition to value-based funding models seeks to lower costs and improve patient care and outcomes in order to lead to the better management of entire populations. Population health management (PHM) involves changing the behavior of engaged consumers to lead healthier lives and encouraging physicians to focus on providing the best possible quality of patient care at the lowest possible price. This requires providers to collaboratively address whole populations and orchestrate healthcare provision at large scale. Below are several challenges organizations must overcome before closing in on the goal of PHM.
Payer-provider collaboration and targeted incentives
Payers and providers must work together and, in particular, must find ways to effectively share their different types of data. Collaboration is needed to achieve shared goals such as understanding and improving the health of a population and enhancing the patient experience, all while constraining costs.
A key issue between payers and providers is agreement on the quality measures that will be incentivized. PHM places an unfair, high burden on providers if required quality measures vary widely across payers or if the measure does not clearly reflect a meaningful quality of care indicator. In the latter situation, a provider’s time and effort are used for inefficient purposes adding to a physician’s frustration with the healthcare system. Conversely, payers have additional data that can often help providers significantly with their population health management needs.
Care coordination
Fragmentation of care poses a challenge for health systems globally, and there is research to suggest that this problem is more persistent in the U.S. than its international peer countries. Studies have highlighted the major consequences of a poorly coordinated health system, including delays in care, incorrect care, and unnecessary complications, tests and procedures. Frequently, poor communication, difficulty sharing care plans and challenges coordinating actions by multiple caregivers across organizations results in confusion, delays in care and even incorrect care actions, putting the patient’s health on the line. A health system that is not well coordinated cannot deliver high quality care at lower costs.
Physician involvement in preventive care and the social determinants of health
For physicians, finding ways to move care from the acute setting toward health promotion, disease prevention and addressing the social determinants of health is quite difficult and not something they are necessarily empowered to do today. Currently, the majority of physicians do not have the tools to solve major intractable social issues such as poverty, so involving physicians and patients in the strategic design of a social determinants of health program is an essential step toward resolution of these types of concerns.
In recent years, the public sector has become increasingly aware of the multifaceted potential of big data. These days, government agencies around the world collect vast amounts of data from people’s activities, behaviors, and interactions—a virtual treasure trove of information that the public sector can utilize and turn into actionable insights, and later, into actual solutions.
The big challenge, however, is that government agencies are falling under more and more pressure to find relevant insights from complex data while relying on limited resources and technologies with inadequate capabilities. And with the sheer size of data being generated nowadays, it’s becoming more difficult to extract meaning from what the data hides beyond their colossal façade.
Thankfully, there is one critical technology that is redefining the way public sector agencies study and utilize data, and this is artificial intelligence. Today, artificial intelligence solutions that make use of technologies like machine learning and topological data analysis can automatically process big data and discover patterns and anomalies no matter how complex and seemingly disjointed these different points of data are.
Precisely because of these advantages, artificial intelligence solutions are now being used by numerous public sector agencies and institutions around the world. In this article, we’ll fill you in on the basics of three important areas of the public sector in which AI is currently making waves.
Financials
The financial industry is one of the biggest producers and exchangers of data, and as such, it stands to benefit immensely from artificial intelligence technologies. Every day, government-owned banks and other financial institutions buy, borrow, and trade currencies and financial products, generating massive amounts of data from customers, partners, and other stakeholders.
By using artificial intelligence solutions for financial services, institutions can develop an intelligence framework that will allow them to cater to customer needs, understand and manage regulatory risks, and proactively prevent money-laundering activities. This way, public sector financial services are able to assume a position of strength over increasingly complex challenges.
Population Health
In many jurisdictions over the world, value-based care is being adopted as an alternative healthcare model that focuses more on accountability, and on the type and quality of service provided to patients instead of the volume of care provided. With this increased emphasis on value, government-aligned health providers and insurance systems have begun using artificial intelligence software in order to become more agile and proficient at managing the risks of patient pools.
This way, even with the immensity and complexity of patient data, public sector providers and health payers are able to better understand clinical variations, as well as automatically predict individual and subpopulation risk and health condition trajectories. Through insights gained from these data, clinicians and other personnel across the healthcare spectrum can then better determine the best and most affordable courses of care, in addition to being able to confidently recommend the most appropriate health programs for their governments to implement.
Guest post by Abhinav Shashank, CEO and co-founder, Innovaccer.
Abhinav Shashank
Time is money, an adage the world follows. When providers realized paper medical records were time-consuming, Electronic Health Records were developed to make things streamlined. Early EHRs were only meant to capture basic clinical information, and over the time EHRs have taken the form of a digital version of paper medical records. In an industry as dynamic and as focused on value as healthcare, it’s not feasible to have physicians spend almost half their time on EHRs.
Challenges physicians face with EHRs
EHRs, in their current state, not only consume a lot of physicians’ time, but they also draw their attention away from their direct interactions with patients. Some of the several significant challenges physicians face are:
Data entry and administrative tasks take up a lot of physicians’ time, according to a study, during the office day, physicians spend as much as 49.2 percent of their time on EHRs.
The demands of desk work and administrative work are not being reconciled with patient priorities and clinical workflows; creating huge gaps between patients and providers. For example, during patient examinations, physicians spend 37 percent of their time on data entry and desk work, compromising on their direct interaction with patients.
Physicians are only reimbursed for face-to-face visits, lab work, and medical procedures and not for EHR tasks. This increases the misalignment in fee-for-service payments and compounds the risk of physician burnout.
Why can’t we do away with EHRs?
While EHRs are not without their own set of challenges, their implementation was necessary, and that still holds true. Only recently, under the Merit-Based Incentive Payment System (MIPS), providers have started to make an effort to enhance value in the care they deliver and the meaningful use of EHRs has been included in MIPS with other substantial quality reporting initiatives. Besides that, there are many offerings of EHRs:
A quick and real-time access to patient records.
Reliable drugs and test prescriptions.
Complete clinical documentation, inclusive of patient medical history.
Accurate and streamlined coding and billing operations.
Reduced cost of operation.
EHR Optimization: Boosting your EHRs
EHR optimization is the process of enhancing and refining the operations of an already installed EHR, to enhance clinical productivity and efficiency. As more and more practices have begun the push for value-based reimbursement, they are demanding more integrated and efficient EHRs.
Opportunities for EHR optimization vary for every practice and range from simple to complex. However, the primary objective of every optimization is reducing the time consumed. Here are some ways healthcare IT platforms can optimize time spent on EHRs for improved patient outcomes:
Establish key performance indicators: Once a healthcare organization has examined its baseline performance, it can decide on goals and target a benchmark for future. Organizations can leverage advanced analytics to determine their progress across each key performance indicator which in turn, helps with quality reporting.
Comprehensive and complete clinical records: It’s important that a patient record is complete- right from their past medical history to their last lab test results. Along with that, if providers are able to look at all vital signs at once, the entire process of designing and implementing a care plan would become efficient.
Implementing clinical decision support: By combining clinical decision support with EHR data, providers can ensure safer and efficient care delivery by documenting every interaction and eliminating redundancies. With every information documented, providers can address the gaps in care well in time.
Sharing vital information across the network: More often than not, the delay in accessing information is the major reason behind improper or delayed care. It’s important that clinical data, lab test results, referrals, etc. are shared across all providers to ensure seamless treatment and population health management.
Monitor, evaluate and maintain results: To ensure the success of optimization isn’t short-lived, providers should continuously monitor their process improvement. Organizations should evaluate their growth and shortfalls and make their efforts to sustain and improve the results they achieve.
Guest post by Abhinav Shashank, CEO and co-founder, Innovaccer.
Abhinav Shashank
The way we see healthcare today is very different from what it was a couple of decades ago. Back then, we did not have the technology to capture the best practices. But, today we have the capability to use medical data as a source of innovation and create impact at scale. But the question is are we capitalizing on it? Have we made the lives easier for both patients and care teams? Are we close to the goals we started chasing ten years ago?
When we talk about innovation in healthcare, we stumble across intuition. The intuition of care teams enhanced by data-driven approaches. It is not just limited providing connectivity to healthcare organizations; it is also about providing advanced analytics and reducing the cumbersome, tedious work! Like deep diving for hours on Excel or making quality tracking and reporting easier.
The concept of population health management is a new one. It has evolved from an idea to become a clinical discipline that works on developing and continually refining measures to improve the health status of populations. A successful population health management program thrives on the vision to deliver robust and coordinated care through a well-managed partnership network. This said, there is no one definition of Population Health Management, fifty different CIOs in an interview gave different definitions to this term. It is a broad concept and covers a lot under its umbrella.
What does an ordinary health IT setup lack?
True, the healthcare systems are working on building the skills to interact and develop well-planned health intervention strategies to move away from the traditional fee-for-service model to value-based reimbursements and incorporating value, but they are falling short in many areas:
Limited EHR capability: EHRs played a pivotal role in digitizing health care, but with EHR technology many restrictions came along. Today, only a few are equipped to support the necessary interoperable standards. To deliver better clinical outcomes, it is of paramount importance that we have the data and right analytics to ensure improvements; something healthcare organizations lack even today.
Integrating data sources: A patient who is being relocated to a new state and will have a new PCP and Care Coordinator. Can we say with confidence that the patient’s information will be available to the new PCP? In a large healthcare network, there is labs, pharmacy, clinical, claims, and operational data, but the capability to integrate it into a single source of truth is still a challenge for many! This has limited the potential of care teams and made them communicate in a disconnected ecosystem.
Risk Stratification: 50 percent of expenditure in healthcare is on 5 percent patient population. Wouldn’t it be great if we could find these patients and cure them before any acute episode? Back in 2012, about 117 million Americans had one or more chronic conditions, and account for 86 percent of the entire healthcare spending. The road to population health management will require care teams to recognize at-risk population timely to reduce cost and improve outcomes!
Guest post by Abhinav Shashank, CEO and c0-founder, Innovaccer.
Abhinav Shashank
Former US President Abraham Lincoln once said, “Give me six hours to chop down a tree and I’ll spend four hours sharpening the ax.” After having a look at the efficiency of the US healthcare system, one cannot help but notice the irony. A country spending $10,345 per person on healthcare shouldn’t be on the last spot of OECD rankings for life expectancy at birth!
A report from Commonwealth Fund points how massive is US healthcare budget. Various US governments have left no stone unturned in becoming the highest spender on healthcare, but have equally managed to see most of its money going down the drain!
Here are some highlights from the report:
The US is third when it comes to public spending on health care. The figure is $4,197 per capita, but it covers only 34 percent of its residents. On the other hand, the UK spends only $2,802 per capita and covers 100 percent of the population.
With $1,074, the US has the second highest private spending on healthcare.
In 2013, US allotted 17.1 percent of its GDP to healthcare, which was highest by any OECD country. In terms of money, this was almost 50 percent more than the country on the second spot.
In the year 2013, the number of practicing physicians in the US was 2.6 per 1000 persons, which is less than the OECD median (3.2).
The infant mortality rate in the US was also higher than other OECD nations.
Sixty-eight percent of the population above 65 in the US is suffering from two or more chronic conditions, which is again the highest among OECD nations.
The major cause of these problems is the lack of knowledge about the population trends. The strategies in place will vibrantly work with the law only if they are designed according to the needs of the people.
What is Population Health Management?
Population health management (PHM) might have been mentioned in ACA (2010), but the meaning of it is lost on many. I feel, the definition of population health, given by Richard J. Gilfillan, president and CEO of Trinity Health, is the most suitable one.
“Population health refers to addressing the health status of a defined population. A population can be defined in many different ways, including demographics, clinical diagnoses, geographic location, etc. Population health management is a clinical discipline that develops, implements and continually refines operational activities that improve the measures of health status for defined populations.”
The true realization of population health management (PHM) is to design a care delivery model that provides quality coordinated care in an efficient manner. Efforts in the right direction are being made, but the tools required for it are much more advanced and most providers lack the resources to own them.
Countless Possibilities
If population health management is in place, technology can be leveraged to find out proactive solutions to acute episodes. Based on past episodes and outcomes, better decision could be made.
The concept of health coaches and care managers can actually be implemented. When a patient is being discharged, care managers can confirm the compliance of the health care plans. They can mitigate the possibility of readmission by keeping up with the needs and appointments of patients. Patients could be reminded about their medications. The linked health coaches could be intimated to further reduce the possibility of readmission.
Over the past few years, healthcare technology has seen many advances. We’ve achieved mass-market adoption of EHRs, many organizations are making meaningful progress on data aggregation and warehousing information from multiple diverse systems, and wearables and other sensors show much potential to unlock personal information about each patient. The pace of change in healthcare is quickening, with each new technology or initiative sending off a chain of reactions across the entire ecosystem, ultimately improving patient care.
I see three trends driving the industry toward change:
Analytics will help predict population heath management
One of the persistent industry challenges is the “datafication” of healthcare. We’re amassing more and more data now than ever before. And new sources (like wearable devices) and new health factors (like DNA) will contribute even more. This data explosion is putting increased pressure on healthcare organizations to effectively make this data useful by delivering efficiency gains, improve quality of care and reduce overall healthcare costs.
Navigating this digitized healthcare environment will require increasingly sophisticated tools to help handle the influx of data and make the overload of healthcare information useful. In 2016, the industry will begin to take concrete steps to transition to a world where every clinician will see a snapshot of each of their patients to help them synthesize the critical clinical information they need to make a care decision. Moreover, hyper-complex algorithms will allow providers not only to know their patients, but to accurately predict their healthcare trajectories. By giving providers insights into how each patient is trending, clinicians will be able to make better-informed, precise decisions in real-time.
Consolidation leads to new healthcare models, improved outcomes
New models for effective population health management continue to drive change across healthcare systems. These models incentivize stakeholders to optimize costs, identify organizational efficiencies and improve decision-making processes to deliver better care at a lower cost through an emphasis on care coordination and collaboration.
