By Abhinav Shashank, CEO and co-founder, Innovaccer.
The Johnsons were blessed with twins the day before; two healthy baby boys, haphazardly named Jill and John in the health records. Definitely, this marks the start of pediatric services in the family. Hospital records set for the twins hardly mark any difference, gender, weight, parents, address; all records read the same. The only visible difference is a skin allergy with the second baby.
Their names were changed to Jack and Ross in a
month, and records got multiplied by two. Vaccinations done within the first
month were registered in the records of Jill and John, while Jack and Daniel
got registered under fresh EHRs.
pediatric space ripe enough for Machine Learning?
How should the healthcare industry deal with
data redundancy or data hop, and maintain data integrity to ensure reliable
records? This is a real serious concern for pediatric organizations.
However, to our rescue is machine learning technology aiding the critical issue of record matching and streamlining medical procedures in child healthcare. ML has the potential to revolutionize the pediatric care ecosystem and assist the major challenges in healthcare operations of the young population.
With the global healthcare market estimated to reach a sweeping $11,908 billion by 2022 and fast-growing problems in the younger population, there is certainly a vast frame of exploration for pediatric focus and care delivery for the young. Being a continuously evolving age group with tailored and sensitive healthcare needs at different stages of growth, the pediatric population is most challenged when it comes to successful reforms and insights.
EHRs doing injustice to the future of healthcare?
Kids from their birthdate are expected to face the EHR duplicity that scatters their record and essential medical data. The key facts of a newborn like weight, height, allergies, among others, are stored in an EHR that is occasionally hopped a month later, with a permanent name signing in.
Once a new EHR is registered with the new name, all medical information of the previous few months gets disconnected. This has a challenging impact on the entire care protocol. The critical notch here is incoherent vaccination and immunization information of the growing baby. Not only does it lead to seemingly real care gaps, but also ripples out to erroneous procedures and increased health costs.
Machine Learning is transforming the way
services are delivered globally. Detecting the minutest of factors in an
outcome, and cascading the learning over huge data, it can provide us with
crucial considerations which are evidently present but still go unnoticed by
us. ML is helping to deliver accurate algorithms for all domains. Applying ML
to pediatric care is sure to transform the current scenario of care delivery
for the younger population.
are the major challenges pediatric organizations are facing?
We need strict adherence and care, not only to
ensure healthy children but also to ensure optimized care procedures for them
in the future. However, there are a lot of shortcomings in understanding and
implementation of the medical requirements of the population aged 0 to 18.
The major challenges in this regard are:
Most pediatric organizations today do not have precise and distinct health measures to evaluate the younger population. We need measures that can efficiently assess the patients on their growth-specific checkers, respectively.
Patient records at different stages are difficult to merge, with inadequate data-merging proficiency.
Data hop in EHRs during record matching or establishment. This is of critical concern for babies and toddlers who need consistent care episodes.
Lack of customized reach to parents for time-sensitive immunization and vaccinations. This leads to missed appointments, which leads to complications and increased costs over time.
Care plans including uncertainties to manage intelligent adherence. This will enable strong network functionality and improved care.
Flexible and optimized timeline for care delivery.
Currently, about 50 percent of children under five years of age attend out of home care. Throughout childhood, children receive care at daycares, check-ups at community places, have physician visits at different pediatric facilities, among others.
It becomes essential to compile entire patient data at a single place to avoid redundant and erroneous procedures. According to the American Health Information Management Association, an average hospital has about a 10 percent duplication rate of patient records. A study by Smart Card Alliance in 2014 projected that about 195,000 deaths occur yearly in the US because of medical error, with 58 percent of them being associated with “incorrect patient” errors.
Machine Learning truly have the answer?
An article in the AAP News and Journals Gateway mentions that only 71.6 percent of young children in the United States have completed their primary immunization series. Moreover, evidence suggests that 10 percent to 20 percent of young children receive more than one unnecessary and extra immunization. Evidently, scattered records lead to a lack of timely, accurate and complete immunization. This can have serious repercussions on the health and care protocol of the patient, in addition to increased medical costs.
Machine Learning can nourish the split needs
and resolve the errors of pediatric healthcare in different domains:
Automatic Triggering for Episodes and Immunization: ML algorithms can be developed to track and prompt parents for necessary episodes and immunization. This will ensure timely care episodes.
EMPI Matching: Enterprise Master Patient Index is a database of medical data across departments and healthcare organizations. Machines trained in pediatric EHRs can develop a robust algorithm to match patient records across hospitals and unify them.
Streamlining Vaccinations: ML algorithms can regularize time-sensitive vaccination arrays for different pediatric categories as decided by the World Health Organization.
Scanning Data Hops: ML algorithms can detect data gaps in procedures, and point out critical consequences enforcing timely merging of EHRs.
Predicting Episodes and Costs: ML algorithms trained with localized pediatric data can detect underlying factors for an episode and predict the average costs for unforeseen episodes.
The pediatric population is foundational to a
healthy nation and demands our attention to reform its split functionalities.
Machine Learning can bring about unimaginable amendments in our current
pediatric care management and delivery. Data, which is foundational to all
ventures in the healthcare industry, can be merged with ML to close all care
gaps and invest in a healthy tomorrow.
By Abhinav Shashank, CEO and co-founder, Innovaccer.
Children have entirely distinctive needs as compared to adults. Care is delivered to them in a manner entirely different than adults by care teams that hardly ever double-up as providers for the elderly.
fact, we hear numerous stories of organizations that transformed their care
delivery by fabricating children-specific strategies and have been really
successful in doing so. However, very few experts ever discuss how little
thought we put when it comes to developing healthcare technologies tailored to the
specific needs of pediatric organizations.
Do pediatric organizations have the
technology to succeed?
2017, more than 95 percent of hospitals had certified EHR technology. However,
these EHRs are heavily adult care centric and may not include measures that are
specific to pediatric populations. In fact, in a recent research piece
conducted on 9,000 pediatric patient safety reports, it was found that about 36 percent of reports were related to EHR
usability has been one of the underrated issues that we need to address if we
are to build an efficient pediatric landscape. This can be attributed to the
fact that even a slight misjudgment in comprehending the information stored in
EHRs can substantially increase the chances of errors and adverse events. The
issue is all lot serious for pediatric organizations where patients are
extremely sensitive to the care provided to them at any given point of time.
Complicated EHRs can do no good to neither children nor pediatricians.
Why is the EHR usability valid ask for
born prematurely have different needs as compared to completely healthy
infants. A 5-year old kid faces problems that a 13-year old teenager does not.
Vaccination once missed can prove costly in the future. A child with Type 1
diabetes may require care plans entirely dissimilar to other children.
Theoretically and practically, each child is unique: from a prematurely-born
child weighing less than a kilogram to an obese 105 kg 14-year-old. The EHR
should be able to ingest all such details with perfection and should provide as
many measures that pediatricians may require.
twin siblings born on the same day, having an identical vaccination cycle, and
same last name. However, they may react differently to various treatments and
have different weight or gender. If they need some medication, they might be
given different mg/dose prescription. Amidst all this, the care teams have the
onus of ensuring that each exercise is taken care of with utmost precision. For
that, they need powerful EHR systems and alert systems, among other things. In
other words, organizations need advanced decision support systems, an ask that
is only valid to deliver value-focused care.
Doctors need reliable EHRs to understand
the complete picture
often than not, there are only two sources of information during any given care
episode — data stored in the EHR and patient’s own words. However,
pediatricians cannot expect much support from their young and very young
patients. For infants, it gets all lot difficult since it gets even harder to
comprehend their symptoms.
such patients, EHRs need to tell the complete picture each time lest errors are
bound to happen. Goes without saying, children are more vulnerable to such
errors as compared to any other patient population. Ideally, pediatric
organizations need to have extremely robust, agile, and accurate EHR systems.
However, the situation is far from ideal even at this age and time.
Pediatric organizations need custom-made
EHRs and IT infrastructure
begin with, EHRs should have an extremely user-friendly interface, support for
adding or converting charts locally for specific syndromes, extremely precise
dosage range, and capabilities to identify missed or pending vaccination. They
should strictly have a pediatric-specific threshold for each symptom,
treatment, or trait, while also having a feature for identifying copied and
newly-added records. Alerts, as discussed earlier, for potentially wrong data
entry should also be a default feature.
with a layer of advanced analytics system on top of their EHRs, pediatricians
can successfully navigate the challenges as they come their way. If pediatric
organizations have a system in place to send regular immunization and wellness
visit reminders, they can both increase adherence rates and reduce potential
access to sensitive patient information and automatic triggers for varying
health trends can further play a substantial role in making care more efficient
for the young. All such steps combined can help us in realizing the dream of
creating the “Internet of Healthcare,” where every stakeholder is connected
with each other and there is a seamless exchange of information at all places
The road ahead
we embark upon the journey of creating an “Internet of Healthcare” where
everyone would be connected with everyone, we first need to have quality IT
infrastructure that can make this possible. EHRs are the building block for
such a system. It’s time we add the human touch to such technological
solutions, and take the first step in the direction of reinventing EHRS.
Healthcare is one of the fastest-growing segments of the digital universe, with data volumes expected to grow by 48 percent annually. Healthcare applications will be the principal driver of this data growth, with EHR penetration in the US already reaching more than 80 percent and expected to reach 95 percent by 2020.
In addition, the healthcare space has matured to the point where EHR replacement has become commonplace, and up to 50 percent of health systems are projected to be on second-generation technology by the year 2020.
So why are these data points an important consideration?
Healthcare organizations have been facing
the major challenge of storing and securing patient information. This is not
just the problem with the providers, but for payers and patients too. While
transitioning to complete digitization of practices, healthcare leaders,
specifically CIOs, often find it a daunting task to identify the areas where
they need to scale up their technological approach.
EHRs are likely the necessary evil for
healthcare. No doubt they solved so many problems; however, they opened gates
to other problems. The complications with the legacy systems compel hospitals
to shift to modern technological solutions.
Right now, the story of mergers and acquisitions in the space is also like an adventure movie. According to KLAS Research, the number of EHR vendors dropped from more than 1,000 to around 400 now — the reason being the rise in mergers and acquisitions.
Where does the actual problem lie?
The journey of shifting from legacy systems
to advanced technology is also ripe with its own set of complications. As the
landscape is molded by M&As, consistent EHR replacements are not rare
In this scenario, organizations face two
Legacy systems have
to be maintained so that organizations are able to access the read-only PHI.
The cost of
migrating data from one EHR to another is unreasonably high.
Moreover, since these EHR replacements are directly linked to the retention of the data from the legacy systems for about a decade. Most states require Protected Health Information (PHI) to be retained for about seven to 10 years.
How is data archival the solution we need now?
Transitioning between EHRs require a
holistic approach to keep their data secure, and the best way here is data
archival. Data archival is a simple process of archiving the entire data from
legacy systems into a unified platform so that it can be kept secured for a
long duration. It is the perfect solution to the above-stated two problems: it
is easier and can be done at one-tenth of the price.
For instance, in the case of legacy systems, the EHR vendor can charge up to $10,000 a month for keeping the system running even after the transition. However, in the case of data archival, this entire process is fast, cheap and much more efficient. Also, it eliminates the necessity of keeping the legacy systems running.
The archiving process serves multiple
functions and has the following major advantages over other data-retention
It allows legal
decommissioning of the legacy systems
It ensures the
integrity of the vital healthcare data
It creates the
opportunity to realize opportunities for immediate Return on Investment (ROI)
It minimizes the
risk of maintaining the historical data
It develops a
centralized repository for all your legacy systems’ data
And many more …
What is the perfect data archival strategy?
The procedure of data archival mainly
consists of two major steps: identifying the need for data archival and
adopting the best archival solution. It is important to analyze the need first
and then take action. It is a complex process and involves complex compliance
requirements to be fulfilled.
So what is needed to be done now? Here is the list of essential prerequisites to be considered and followed religiously before archiving your crucial healthcare data:
Understand your healthcare data
The first step is to understand your EHR and legacy system data. One organization might be focusing on archiving the data from a single EHR while the other might be looking for a solution that can archive the data from multiple data sources. Everyone’s data needs are different and, thus, requires a different data archival approach.
Familiarize yourself with your state regulations
Every state has its own regulations to archive the data. The state of California might need you to archive your data for six years, while the state of Minnesota might have a span of more than 30 years. These regulations need to be considered and understood efficiently before investing in a data archival solution.
Chalk out your technological requirements
The next and
most important step is to identify the extent and the varieties of
technological features your organization might need. Every organization has
different needs which should be analyzed and understood well in advance. Based
on these insights, the final decision can be made about any data archival
solution and its abilities.
The road ahead
The space of healthcare is among the most diverse and ever-changing fields. New mergers, efforts towards making the practice data-driven, empowering providers with access to every single bit of data about their patients, and whatnot; these factors have compelled organizations to keep shifting towards a better option — a better EHR. And in this story, the ultimate goal is to make this transition as smooth as possible. It is important to ensure that organizations get rid of all their legacy system headaches instantly. With data archival, it is finally possible.
The world of healthcare is changing and those changes impact how we deliver care, our approach to engaging patients and the relationships between stakeholders across the healthcare value chain. Each day, we witness advances in genomics, imaging and pharmacology, and learn about the use of artificial intelligence (AI) to drive these advances. Indeed, healthcare is in the midst of a major revolution and AI seems to be at the very core of this transformation. How much of the AI story is hype and how much is real?
Innovaccer Inc., a San Francisco-based healthcare data activation company, is hosting a breakthrough AI webinar on June 20 with guest speakers Dr. Peter Lee, corporate vice president, Microsoft Healthcare, and Stephen K. Klasko MD, MBA, president and CEO, Thomas Jefferson University and Jefferson Health, who will be discussing the new healthcare domains of AI, and it’s “never imagined” impact. They will be joined by webinar moderator, David Nace MD, chief medical officer at Innovaccer.
The use of AI in healthcare has lagged behind other industries, in large part because of the lack of comprehensive, pristine data. The webinar, titled “Beyond Interoperability: Data Activation and Artificial Intelligence for Healthcare,” will focus on the recent AI hype, tease fact from fiction, and explain how advances in data activation can solve the accuracy and interoperability problems in the space.
Dr. Lee has extensive experience in managing the process of going from basic research to commercial impact. Past illustrative examples include the deep neural networks for simultaneous language translation in Skype, next-generation IoT technologies, and innovative silicon and post-silicon computer architectures for Microsoft’s cloud. He also has a history of advancing more “out of the box” technical efforts, such as experimental under-sea data centers, augmented-reality experiences for HoloLens and VR devices, digital storage in DNA, and social chatbots such as XiaoIce and Tay.
Lee is a member of the board of directors for the Allen Institute for Artificial Intelligence and the Kaiser Permanente School of Medicine. He served on President’s Commission on Enhancing National Cybersecurity. And, previously, as an office director at DARPA, he led efforts that created operational capabilities in advanced machine learning, crowdsourcing, and big-data analytics, such as the DARPA Network Challenge and Nexus 7.
Under Dr. Klasko’s leadership, Jefferson Health has grown from three hospitals in 2015 to 14 hospitals today. His 2017 merger of Thomas Jefferson University with Philadelphia University created a pre-eminent professional university that includes top-20 programs in fashion, design and health professions, coupled with the first design-thinking curriculum in a medical school, conducting the nation’s leading research on empathy, an essential component of medicinal practice that is often overlooked in the academic setting. As a disruptive leader in the academic ecosystem, Dr. Klasko brings a valuable point of view to the Innovaccer Strategic Advisory Council.
By Abhinav Shashank, co-founder and CEO, Innovaccer.
While healthcare leaders uniformly agree that transitioning to value is the way healthcare is going to be in the coming days, it is unclear to most how they can make the transition without negatively impacting their cost outcomes. In an industry which had primarily been fee-for-service based, healthcare organizations are facing immense pressure to innovate and adapt or risk their long-term viability.
In developing strategies to succeed with these trends, many healthcare leaders are realizing that Medicare Advantage (MA) is a key component to their long-term success. The Centers for Medicare and Medicaid Services (CMS) has projected that Medicare Advantage enrollment will reach an “all-time high” in 2019 with 22.6 million Medicare beneficiaries, given the unprecedented growth. And industry analysts like L.E.K. Consulting say that Medicare Advantage enrollment will rise to 38 million, or 50 percent market penetration by the end of 2025.
Going along the same lines of ensuring long-term success and enhanced patient satisfaction, CMS rates Medicare Advantage plans by giving them Star Ratings which help beneficiaries and their family members make informed decisions. As MA Star Ratings become the most visible mark of success, the only trail of thoughts would be: How to improve these Star Ratings?
How do Star Ratings work?
The Medicare Star Ratings are key measures of the quality of care a health plan provides. The health plans are rated on 45 measures categorized under five categories which portray how a health plan takes care of its beneficiaries.
Needless to say, there’s a lot at stake here. The more Stars a health plan has, the more likely they are to attract beneficiaries. But earning top ratings is a difficult task. Payers that wish to reap the benefits of high Star Ratings also need to deliver impeccable care to their members and ensure a satisfactory experience of care.
What holds MA Plans back from achieving better Star Ratings?
A majority of these measures are defined on the basis of specific service received, claims, or clinical information that verifies access and delivery of care. For example, if there is a large number of members that have a chronic disease, plans can pinpoint them and identify the specific care they have received during the year. After that, they can plan targeted interventions to close the gaps and be on the path to deliver positive outcomes.
However, with limited actionable member data available, MA plans just end up focusing on broad, general interventions as compared to undertaking a member-specific, targeted approach. MA plans require timely and detailed information about their members’ health to create interventions that have a lasting impact.
Additionally, it’s important to realize that beneficiaries don’t just have high-quality care, but also have quick access to healthcare service. MA plans need to ensure that the quality of care is always upheld. In most cases, it stems out of efficient collaboration between the clinical staff and healthcare technology.
More importantly, improvements in Star Ratings depend significantly on how engaged a patient is. For example, measures which are related to medication adherence are almost completely hinged on strong patient engagement that makes it easier for patients to get access to their medications and take them on time. In other words, MA Plans need to deploy efforts that are aimed at implementing holistic strategies to address patient needs.
By Abhinav Shashank, president and co-founder, Innovaccer.
What makes anyone identify the best health plan for themselves? In today’s world, having health insurance is very important. You might end up paying significantly more for a doctor’s visit if you don’t have insurance than if you had it. You could rack up paying hundreds of dollars for a major injury or if you go for a costly treatment. And in this flock of health insurances, employer-sponsored health plans make up a significant percentage.
How does employer-sponsored health plans fit into the situation?
Employee health and well-being is not just essential, but also foundational to business success. Only a healthy team could deliver profitable outcomes. For this reason, among the list of many, most of employed Americans have their health insurance covered by their employers.
According to a survey, 92 percent of respondents were confident that their organization will continue to sponsor health care benefits for the next five years.
High-performance Insights- Best Practices in Health Care, 2017 22nd Annual Willis Towers Watson Best Practices in Health Care Employer Services
Is employer-sponsored healthcare on the verge of breaking or is it broken already?
Employer-provided healthcare is underleveraged. Currently, employer-sponsored healthcare is facing a lot of complications, including:
New market entrants add more complexity to employer decisions
As financial responsibility for care shifts to employees, an increase in self-rationing may drive poor outcomes
Pharmacy remains an area of unchecked rising cost, especially with regard to high- cost biogenetic (specialty) drugs, among many
What is haunting the large employers and how is the market ripe for innovation?
“Interestingly, 70 percent of employers believe new market entrants from outside the healthcare industry are needed to disrupt health care in a positive way. These disruptors include innovators from Silicon Valley and elsewhere, and employer coalitions,” said Brian Marcotte, president and CEO, National Group on Health.
Fifty-five percent of employers are concerned about prescription opioid abuse and working with partners to implement safe prescribing patterns and alternative therapies. The innovation we need to resolve this issue starts with data. With the launch of CURES 2.0 database, healthcare in the state of California achieved a milestone in curbing the opioid epidemic.
The role of activated data in enhancing the employer-sponsored health plan is that of an initiator to a revolutionary change in the field. Once the organizations have the right data, they can gain crucial insights into their employees and devise better plans to enhance their health and productivity.
What causes two patients of the same age and with the same disease but from different regions to respond differently to a certain treatment? Even if these two patients appeared similar on paper, their lifestyles are very likely to differ — socioeconomic status, gender, race, ethnicity, family structure and education.
Is SDOH a promise for a better future, or is it just another hype?
Success in the value-based care environment cannot be achieved based solely on clinical insights. According to one study, clinical care accounts for only 20 percent of the health outcomes of patients, while health behaviors, social and economic factors, and physical environment combined add up to in?uence the remaining 80 percent of health outcomes.
Social determinants matter because they can affect the health of the population residing in a particular region for better or for worse. Trying to improve population health armed with only clinical data and not the non-clinical factors, is like investing in a project which cannot generate positive returns.
Although multiple pieces of research demonstrate that social determinants may substantially contribute to a person’s health status and well-being, the major problems are these:
How do we address these complex challenges?
Who is the best-positioned stakeholder to do so in a clinical environment?
What is the right way to address these social determinants?
The Centers for Disease Control and Prevention (CDC) has defined an algorithm to estimate the Social Vulnerability Index (SVI) for every census-tract in the US. However, this algorithm is based on a simple summation of the percentile ranks for all SDOHs, which results in an over-estimation of social vulnerability in cases of high positive correlation between multiple SDOHs.
Working with SDOH data requires a more drilled-down approach and the use of predictive analytics to accurately measure the at-risk population and to advance preventive care methods in an ecosystem.
The right approach is to start from a state-level analysis and drill down to the zip code-level. The effects of social determinants vary in accordance with a very small region. There is a high possibility that all the zip codes in a county will have different susceptibility to a particular social determinant.
What new ways can a revolutionary approach to SDOH open for healthcare?
Every social determinant affects the region in its own way and corresponding preventive actions need to be taken in order to overcome the adverse health outcomes of the citizens of that region. For instance, community resources and data needs to be integrated into the care coordination processes to make proper interventions. When providers are able to completely understand the effects of non-clinical factors, they can provide much better care to their patients.
The analysis of social determinants can be applied for multiple use cases such as:
Identifying the role of behavioral health, social workers and health coaches
Increasing the efficiency of the care coordination team
Forging better partnerships with community resources and social improvement funding agencies, and many more.
The road ahead
Though providers have recognized that social factors significantly influence their patients’ health, they are often unaware of their patients’ social vulnerabilities and are unable to accept responsibility for managing these issues or providing support to their patients outside of the clinical realm. We are stepping into the age of predicting and preventing diseases instead of curing them. That was the traditional approach. With non-clinical data and resources such as SDOH, we can change the future of US healthcare. All we need is the will to right these wrongs.
By Abhinav Shashank, CEO and co-founder, Innovaccer.
Once while I was scrolling through the news feed on my phone, there was one specific line that really made me wonder: “There’s a 40 percent chance of gusty and blustery winds today.” Statements such as this one strongly influence people’s behavior, as they are based on evidence or data findings from years of surveying, studying, and analyzing past trends and occurrences. However, my question is “Why are we not able to make such claims in healthcare- even today?”
Can we predict the vulnerabilities a patient might face in the future or the current health risks a population segment faces?
Is risk scoring the answer we have been looking for?
Almost all kinds of care organizations have some risk scoring methodology to target care interventions. With quality, costs, and patient experience taking the center stage in healthcare, care organizations need to stratify patients based on their need for immediate intervention.
The need of the hour is to address high-risk issues that impact large groups of patients and ensure that these needs are met in a timely fashion. Often, frequent fliers among high-risk patients come into the emergency department as if it’s their second home.
What if we take the method of risk scoring to a whole new level?
Traditionally, providers and health systems have relied on claims-based risk models, such as CMS-HCC, ACG and DxCG, which were built to forecast the risk of populations/sub-populations but not for individual patients. Hence, these models give an accurate prediction of the average risk of the population but exhibit very poor accuracy if used to predict risk for individual patients.
Although risk scoring has turned out to be a key factor in addressing the needs of the patient population, this method cannot provide all the important insights that are needed to drive necessary interventions. Since healthcare already has the right data from sources such as EHRs, claims, labs, pharmacy, social determinants of health (SDoH) and others, can we predict the future cost of care instead of just stating the risk score of the patient?
The right machine learning-driven approach to predict the future cost of care for patients
It all starts with the right data. The first step is to integrate the data from multiple sources- whether it is clinical or non-clinical data, such as SDoH. The data from these sources can allow us to use the comprehensive patient’s data for multiple predictive models to predict future health cost with greater accuracy.
By Abhinav Shashank, CEO and co-founder, Innovaccer.
Articles on social media channels that carry a sense of apprehension regarding the future of our healthcare system sadden me. However, I learned a long time ago that you never win by arguing with the referee, and that the most logical way to react to apprehensions is to prove them wrong based on concrete evidence.
Building a sustainable model for care delivery is not a tough nut to crack as long as organizations have the right approach. If healthcare leaders can adapt to the constantly changing needs of providers and payers alike, they can steer their organizations towards a better future.
What if we already know all the answers?
Patient outcomes depend on a number of factors. I know cities with poor air quality have a higher percentage of patients with lung-related diseases than the green countryside. Similarly, patients who follow-up with their doctors more often usually take less time to recover from a problem as compared to less engaged patients.
Care management is one area I genuinely believe is an answer to a plethora of problems that surround our healthcare system. However, enabling a culture of managed care is easier said than done. To begin with, it is quintessential to make providers and patients believe in its very significance. This can be achieved by promoting patient engagement, streamlining referrals, increasing annual wellness visits, and regular follow-up meetings, among others.
Creating pathways for automated care management procedures
Baby boomers, millennials, middle-aged people, and kids? everyone has different needs and expectations. However, every patient longs for comfortable, connected, and cost-effective care.
Continuity of care is the key here. Care delivery is an end-to-end process. Care coordination and its various domains? transitional, chronic, and post-acute, among others? holds the potential to improve care and cost outcomes drastically. The more providers know about their patients, the easier it gets to impart care in a much more personalized and evidence-based manner.
Making things easier for patients shouldn’t come at the cost of frustrated providers. Provider and patient satisfaction are, in fact, interdependent. For instance, organizations should ensure that there are little or no skipped appointments and at the same time, calling patients to remind them of their scheduled meetings should be the least of providers’ concerns.
Non-clinical factors can account for up to 80 percent of the health outcomes for patients. Such factors, including socioeconomic conditions, healthy behaviors, and physical environment, may vary drastically for each patient and can significantly impact health outcomes such as poor medication adherence, frequent visits to the ED, and more. Thus, it is essential to consider these factors while creating care plans to ensure that the specific needs of patients are addressed.
Additionally, healthcare’s transition to value-based care is pushing organizations to lead more efficient population health management programs that address every clinical and social need of the population in which they serve. The challenge, however, is that organizations don’t usually have the means to capture the social needs of the patients or address them beyond the four walls of a hospital to ensure that no care gaps remain unplugged.
Innovaccer offers to assist healthcare organizations in a stepwise approach, starting with surveys for patients to complete in order to evaluate their social needs, such as access to food, housing situations, or economic conditions. Additionally, Innovaccer’s solution allows care teams to send as many surveys as needed with multiple language support. Based on the answers received from the survey, the solution helps care teams find suitable community resources to assign to the patient from a pre-built national database.
The solution’s AI-assisted closed-loop referral process to community resources enables care teams to ensure patient-centric care, even after an encounter is over. This closed-loop referral process gives physicians and social workers complete visibility into the social needs of their patients, which allows them to refer their patients to the most relevant community resources. In fact, patients are also kept in the loop in such a way that they can track their referrals, give feedback, and coordinate with their providers at any time, all through a single mobile application.
Innovaccer’s primary aim with this solution is to empower physicians and care teams with visibility into the social needs of their patients, right in the moment of care. The solution also triggers automated and real-time alerts to care teams if a patient’s needs are found to be urgent, such as high social risk or missed follow up. Additionally, the insights from the survey are available to the physicians right at the point of care within their EHR workflows, ensuring that they have a holistic picture of their patients.
“For organizations under value-based contracts, establishing a culture of wellness is a priority to keep their business model financially viable. Social determinants of health are a gamechanger in this regard and organizations who leverage them put themselves in the driver’s seat,” said Abhinav Shashank, CEO at Innovaccer. “We hope that our solution is instrumental to healthcare organizations as they tie their efforts to address social determinants of health and create similar strategies to maximize care and cost outcomes.”
Only recently, Innovaccer also launched its first-ever in-house research authored by Dr. David Nace, CMO at Innovaccer, around the social vulnerabilities of the population across the US. The research paper named “From Myth to Reality- Revolutionizing Healthcare with Augmented Intelligence and Social Determinants of Health” discusses a revolutionary way of leveraging advanced algorithms to determine the social vulnerability of the zip code-level population.
To learn more about Innovaccer’s SDOH Management solution, click here.