One of the greatest challenges in healthcare is keeping up with the changing landscape. Considering only since the beginning of 2019, the Centers for Medicare and Medicaid Services (CMS) and other federal agencies, such as the Office of National Coordinator of Health IT (ONC) and the Department of Health and Human Services (HHS), have introduced a number of rules as a measure of upholding their goal of empowering patients and enhancing healthcare efficiency. We’re at a very critical juncture in healthcare and from a regulatory perspective, there are a few key rules that merit a special focus which will have a great impact from both a clinical and financial standpoint.
The MyHealthEData Initiative in 2019
The MyHealthEData initiative, launched in March 2018, aims to “empower patients by ensuring that they control their healthcare data and can decide how their data is going to be used, all while keeping that information safe and secure.” Only a few days back, CMS upped the ante for better data access by expanding this initiative and announcing the pilot of “Data at the Point of Care.”
The Data at the Point of Care (DPC) pilot will be connecting providers with Blue Button data, where providers can access claims data to learn more about their patients and their previous diagnoses, procedures, and prescriptions. While providers had to comb through several hundred data sets previously, the DPC program would aim to make access to data easier and right within their workflows.
This announcement follows the relaunch of the Blue Button initiative, or Blue Button 2.0, that grants access to health data and enables patients to send that information using FHIR-based healthcare apps.
In a nutshell, these moves come as an overall push from CMS to promote better access to data and 100% healthcare interoperability. In addition to enabling data access, CMS has also been targeting information blocking, as reflected by 2019 MyHealthEData updates. With these measures, both patients and providers will have the required insights to make more informed healthcare decisions.
The Trusted Exchange Framework and Common Agreement
In April 2019, ONC published its second draft of the Trusted Exchange Framework and Common Agreement (TEFCA), focusing on three high-level goals:
Providing a single ‘on-ramp’ to nationwide connectivity
Enabling Electronic Health Information (EHI) to securely follow the patient wherever needed
Supporting nationwide scalability
TEFCA is basically a common set of principles which serve as “rules of the road” for nationwide electronic health information exchange across disparate health information networks (HINs). The framework, which was mandated by the 21st Century Cures Act, provides a set of policies and procedures along with technical standards required to enable healthcare data exchange among providers, state and regional HINs, and federal agencies.
By Abhinav Shashank, CEO and co-founder, Innovaccer.
Fact 1: As per the latest data made available by the Office for Civil Rights for HHS, more than 208,000 privacy-related complaints have been made in the last 16 years.
Fact 2: If a hospital makes a call to a patient
to remind them of their upcoming appointment, they might receive a class action
complaint about violating the Telephone Consumer Protection Act.
these two facts may not necessarily be related to one another, we clearly need
to take a hard look at the increasing calls to protect patient privacy. But
does that mean providers cannot send a text message to their patients?
California’s latest policy for text
message technology for Medicaid plans: A case study
1991 Telephone Consumer Protection Act (TCPA), which was put in place to
safeguard people from automated text or other telephonic messages, limits
organizations from reaching out to their patients through text messages. TCPA
can also levy financial penalties on organizations if they are found guilty of
violating their policies. On the other hand, the Health Insurance Portability
and Accountability Act of 1996, or HIPAA, require every “Covered Entity or
Business Associate that comes into contact with Protected Health Information
(PHI)” to follow the compliance policies, something that is accepted as a rule
of thumb in the healthcare world. For any organization looking to reach out to
patients remotely, both HIPAA and TCPA policies are extremely important to
comprehend and follow.
today’s context where patient engagement through text messages has emerged as
one of the biggest avenues for optimizing care quality, the TCPA is losing its
sheen to some extent in the healthcare domain. While no one denies the
importance of TCPA, it does cause some roadblocks for organizations looking to
enhance patient engagement in remote areas and population segments.
California Department of Health Care Services (DHCS) recently issued a policy
to set guidelines regarding how Medicaid plans can safely use the text
messaging technology to connect with beneficiaries. This is critical since one
out of three people in California are Medicaid beneficiaries.
latest ruling allows organizations to reach out to their patients through text
messaging after submitting an approval form to the concerned regulators clearly
mentioning the structure as well as the intent of such reach out campaigns.
They also need to create proper avenues for privacy protection and give users a
clear opt-out option. However, once such campaigns are approved, the payer can
then run such programs without any additional regulatory clearances. Further,
such outreach messages must be made available at no cost to Medicaid members.
What can we learn from the example of
According to a study, hospitals could reduce their discharge time by 50 percent if conducted by secure text messaging, saving healthcare facilities an average of $557,253 per year.
text messaging is indeed a big deal. Make no mistakes, privacy and security
should still remain the top-most priority while enabling such mechanisms, and
password protection is something that we should all consider. However, in an
age when we are shifting our focus on precision medicine and advanced robotic
surgeries, the ability to create a secure system for text reminders should not
be a big deal.
text message service is indeed the most prevalent form of communication for Americans
younger than 50, and about 80% of people state it as the preferred way of receiving
notifications. The latest DHCS policy will empower payers to connect with their
populations like never before, an ability that would allow them to initiative
preventive care and scheduling, while ultimately reducing care and cost and
improving outcomes. It can be safely assumed that the latest initiative by DHCS
is a breakthrough step in this direction.
Organizations need the ability to meet
their patients where they want
remember one of my friends asking me a very simple yet important question, “If
I can connect with my colleague based out of London in literally 10 seconds,
why does it take my provider so long to tell me that my appointment has been
canceled?” I had no answer.
cannot expect a person whose calendar is booked for the next 10 days to walk
into a clinic for a regular check-up and wait idly for a couple of hours due to
inefficient scheduling practices. Worse still, imagine a situation where a
person takes time out to visit a facility for their Annual Wellness Visit (AWV)
only to find out that their appointment has been rescheduled for the next week.
A simple suggestion of taking aspirin as a first-aid measure in a potential case of a heart attack sent through an SMS on your way to the hospital can help a patient significantly reduce the damage. Remote patient outreach is an important prospect for today’s practices, if not a necessity. It’s really that simple — connect with your patients to know them better, to treat them better, and to make them feel better with minimum interventions. While organizations can still sustain under value-driven contracts without such streamlined patient communication mediums, we cannot keep believing that we would cross that bridge when we come to it.
The road ahead
in healthcare was never a widely-discussed topic until very recently, however,
things are changing and how! Innovating while respecting the mandates in place
should be the road ahead, definitely. The government is supporting new-age
initiatives, federal healthcare agencies are bringing in new policies, and
large payer and provider organizations are exploring ways to maximize patient
satisfaction. Examples set by organizations such as DHCS will act as an ice
breaker for other agencies and organizations wanting to break free to cater to
the unique needs of the 2020s.
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.
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.