Innovaccer is a healthcare technology company pioneering the Data Activation Platform that’s helping the industry realize the promise of value-based care.
Innovaccer’s integration & analysis engine activates healthcare data, cleaning, aggregating and delivering insights at the moment of care. This revolutionary technology streams analytics with custom insights and dashboards, automates workflows, provides real-time decisions for care teams, and point-of-care alerts—actionable intelligence without leaving the EHR experience.
Innovaccer is based in San Francisco with offices across the United States and Asia.
What is the single-most innovative technology you are currently delivering to health systems or medical groups?
Innovaccer is a leading healthcare technology company that deploys its FHIR-enabled Data Activation Platform to help the healthcare industry realize the promise of value-based care. The name “Innovaccer,” is, in fact, a play on the words “innovation” and accelerator.”
Innovaccer leverages AI and predictive analytics to generate insights that help healthcare organizations achieve better clinical outcomes. The FHIR-enabled Data Activation Platform is built on a Hadoop-based Big Data repository with a scalable architecture that allows the integration of disparate sources of data without having to write code. Its agile and modular structure can ingest structured, semi-structured, unstructured data, pool it as a single source of truth, and work on a central HL7 FHIR-based data schema.
How is your product or service innovating the work being done in the organization to provide care or make systems run smoother?
Innovaccer’s smart FHIR-enabled Data Activation Platform has intelligent workflows powered by unified patient records, advanced analytics and true interoperability, enabling collaborative healthcare. Innovaccer brings the data and all healthcare stakeholders together and empowers them with complete patient information to help them care as one.
Today, Innovaccer’s COVID-19 Management System uses AI to optimize the provider response to the disease, allowing medical facilities to reduce assessment time and prioritize patients with a high-risk profile for the next steps of care.
Healthcare is home to some of the most mind-blowing technological advances when it comes to diagnostics and therapies. At the same time, the healthcare system is responsible for many of the most head-scratching operational difficulties related to standard IT processes, such as those involved with moving data from one system or site to another. The same industry that successfully deploys remote-controlled surgery robots to heal a patient also struggles to send a discharge summary to a physical therapist for the same patient.
How can we explain this apparent paradox?
A Model of Interoperability
The simple answer is that interoperability in healthcare is a journey, not a destination. The question “why haven’t we solved interoperability?” assumes that interoperability is a one-time problem, when in fact the systems, standards, and data flows that constitute interoperability are constantly changing as the underlying patterns of treatment and reimbursement change.
Interoperability today spans more systems and settings than ever before, and “interoperability” means different things to different audiences, so it’s worth taking a moment to ponder the modern interoperability landscape. The following model is an expansion of one suggested by the National Academy of Medicine.
In the center, provider, or organizational, interoperability indicates data flows within a single organization, typically a hospital or hospital system. This has historically been dominated by HL7 v2 transmitted over a TCP connection on a private network, although other standards and technologies are also used.
At the top, community, regional, and national cross-organizational Interoperability refers to communication of healthcare data across different organizations, for example ACOs, HIEs, and provider-to-payer data flows. In this part of the model, we often see IHE style integrations that exchange entire patient records in a single, secure transaction over a public network.
At the bottom, the Internet of Medical Things (IoMT) is divided into two parts: medical device interoperability, which concerns devices used in a clinical setting, and consumer device interoperability – commercially available devices marketed directly to consumers, such as fitness devices and in-home monitors.
On the left, healthcare IT vendors need to enable their products to interoperate at different levels of the interoperability model, depending on which markets they serve. For example, an EHR vendor will need its product to send and receive HL7 v2 messages in order to participate in the organizational interoperability space.
On the right, we are seeing an increased demand for patient or consumer access to their data, whether it is from a fitness device, a hospital’s EHR, or a payer’s disease management system. Integration in this box is often accomplished using APIs, including FHIR.
By Patricia Hyle, vice president of product commercialization, StayWell.
Fast Healthcare Interoperability Resources or FHIR was introduced in 2014 as a data standard for electronic health records to adopt, enabling improved access in sharing health data. The move was predicated by new standards set for the with the passing of the Affordable Care Act in 2015 but supports the standard framework for EHR systems to ensure patient information be accessible in an effort to deliver quality care.
FHIR aims to simplify implementation without sacrificing information integrity. It leverages existing patient models to provide a consistent, easy to implement, and rigorous mechanism for exchanging data between EHR applications. This move gained ground when it earned support and adoption from Epic and Cerner, two of the largest EHR systems in the industry. With more than 80 percent of hospitals and health systems now using EHRs on the FHIR platform, it has become the standard for EHR vendors to meet ONC certification criteria.
Addition of apps to FHIR
Following the adoption of FHIR as the new universal standard operating platforms for EHR systems, the launch of SMART (Substitutable Medical Applications, Reusable Technologies) quickly followed to enable to launch of apps within the FHIR platform. When the two platforms came together it became known as SMART on FHIR, allowing software engineers and clinicians to create open-source tools for app developers.
With the addition of these plug-in apps, clinicians can pick and choose which apps they want to integrate into their EHR system. This allows apps to use the standard type of data to build profiles, deliver tools, create reminders, or share data within a fully connected set.
One example of this type of app on is Krames On FHIR. Launched in 2017, this app delivers recommended patient education materials based on inputs from the patient’s EHR record. The material also includes video resources, interactive tools, and health tips that can be sent directly to patients via the patient portal and provides a patient engagement dashboard that allows physicians to track engagement for greater treatment adherence.
Impact of consumer applications to FHIR
When SMART on FHIR initially launched, it was intended to be a set of app standards for developing apps within the closed FHIR network. The end user would be interfacing with an EHR system, and ideally the end user was a care provider or administrator in a health care setting. However, after a few years of use, more users and developers saw the potential use of extending limited access to the network to the patient.
Partners HealthCare announced its selections for the fifth annual “Disruptive Dozen,” the 12 emerging artificial intelligence (AI) technologies with the greatest potential to impact healthcare in the next year. The technologies were featured as part of the World Medical Innovation Forum held in Boston to examine AI in clinical care including a range of diseases and health system opportunities.
“Understanding state-of-the-art medical technologies enables us to anticipate the future of clinical care,” said Gregg Meyer, MD, chief clinical officer, Partners HealthCare and 2019 World Forum co-chair. “The Disruptive Dozen technologies can offer physicians and patients a renewed sense of optimism about Artificial Intelligence and its impact on diagnosis and treatment.”
The 2019 Partners HealthCare Disruptive Dozen are:
1 Reimagining medical imaging – AI is transforming radiology and imaging, including mammography and ultrasound, to bring improvements in clinical care and diagnoses to patients worldwide. Researchers envision AI transforming mammography from one-size-fits-all to a more targeted tool for assessing breast cancer risk, and further increasing utility for ultrasound for disease detection and rapid acquisition of clinical-grade images.
2 Better prediction of suicide risk – Suicide is the 10th leading cause of death in the U.S. and the second leading cause of death among young people. AI is proving powerful in helping identify patients at risk of suicide (based on EHR data,) and also examining social media content with the goal of detecting early warning signs of suicide. These efforts toward an early warning system could help alert physicians, mental health professionals and family members when someone in their care needs help. These technologies are under development and not cleared for clinical use.
3 Streamlining diagnosis – The application of AI in clinical workflows such as imaging and pathology is ushering in a new era of AI-enabled disease diagnosis. From identifying abnormal and potentially life-threatening findings in medical imaging, to screening pathology cases according to the presence of urgent findings such as cancer cells, AI is poised to aid the diagnostic, prognostic, and treatment decisions that clinicians make while caring for patients.
4 Automated malaria detection — Nearly half a million people succumbed to malaria in 2017, with the majority being children under five. Deep learning technologies are helping automate malaria diagnosis, with software to detect and quantify malaria parasites with 90 percent accuracy and specificity. Such an automated approach to malaria detection and diagnosis could benefit millions of people worldwide by helping to deliver more accurate and timely diagnoses and could enable better monitoring of treatment efficacy.
5 Real-time monitoring and analysis of brain health – a window on the brain – A new world of real-time monitoring of the brain promises to dramatically improve patient care. By automating the manual and painstaking analysis of EEGs and other high-frequency wave forms, clinicians can rapidly detect electrical abnormalities that signal trouble. Deep learning algorithms based on terabytes of EEG data are helping to automatically detect seizures in the critically ill, regardless of the underlying cause of illness.
6 “A-Eye”: Artificial intelligence for eye health and disease – Not only is AI is helping advance new approaches in ophthalmology, it’s demonstrating the ability of AI-enabled technologies to enhance primary care with specialty level diagnostics. In 2018, the Food and Drug Administration approved a new AI-based system for the detection of diabetic retinopathy, marking the first fully automated, AI-based diagnostic tool approved for market in the U.S. that does not require additional expert review. The technology could also play a role in low-resource settings, where access to ophthalmologic care may be limited.
7 Lighting a “FHIR” under health information exchange — A new data standard, known as the Fast Healthcare Interoperability Resources (FHIR) has become the de facto standard for sharing medical and other health-related information. With its modern, web-based approach to health information exchange, FHIR promises to enable a new world of possibilities rooted in patient-centered care. While this new world is just emerging, it promises to give patients unfettered access to their own health information — allowing them to decide what they want to share and with whom and demanding careful consideration of data privacy and security.
8 Reducing the burden of healthcare administration — use of AI to automate routine and highly repetitious administrative functions. In the U.S., more than 25 percent of healthcare expenditures are due to administrative costs, far surpassing all other developed nations. One important area where AI could have a sizeable impact is medical coding and billing, where AI can develop automated approaches. The goal is to help reduce the complexity of the coding and billing process thereby reducing the number of mistakes and minimize the need for intense regulatory oversight.
9 A revolution in acute stroke care — Stroke is a major cause of death and disability across the world and a significant source of healthcare spending. Each year in the U.S., nearly 800,000 people suffer from a stroke, with a cost of roughly $34 billion. AI tools to help automate the diagnostic journey of ischemic stroke can help determine whether there is bleeding within the brain — a crucial early insight that helps doctors select the proper treatment. These algorithms can automatically review a patient’s head CT scan to identify a cerebral hemorrhage as well as help localize its source and determine the volume of brain tissue affected.
10 The hidden signs of intimate partner violence – Researchers are working to develop AI-enabled tools that can help alert clinicians if a patient’s injuries likely stem from intimate partner violence (IPV). Through an AI-enabled system, they hope to help break the silence that surrounds IPV by empowering clinicians with powerful, data-driven tools. While screening for intimate partner violence (IPV) can help detect and prevent future violence, less than 30 percent of IPV cases seen in the ER are appropriately flagged as abuse-related. Healthcare providers are optimistic that AI tools will further complement their role as a trusted source for divulging abuse.
The healthcare system seems to become better every day. The growth and development of digital platforms have provided a unique dimension for this particular industry to grow tremendously and provide an excellent service to the seekers. In this context, Apple, the most reputed smart phone and electronics manufacturer in the world, has launched a brilliant personal health record (PHR) platform in beta-version. This new version of PHR is released as part of iOS version 11.3.
This Apple health news is the beginning of a new era where the patients can easily share their personal health information with the entitled service providers. This step can be initiated by hospitals and other healthcare related firms so as to provide the best and safest treatment solutions. An iOS App Development Company will not become sufficient to provide such elegant applications that will maintain the industry standards and meet with the specifications of the contemporary healthcare system.
Apple’s new venture
For the very first time, the world’s largest manufacturer of smart phones, Apple has launched the beta version of a personal health record application. Currently, it has incorporated this application in iOS version 11.3. It has developed this unique platform based on the specifications provided by Fast Healthcare Interoperability Resources (FHIR) constructed by HL7, a non-profit standard developing organization. It is considered to be the new face of health records app. This application is being developed and advanced in collaboration with 12 hospitals such as Cedars Sinai, Penn Medicine, Geisinger Health System, and John Hopkins.
Jeff Williams, the COO of Apple, said that the prime motto of this particular application is to aid the consumers spread across the world to lead a better and safer life. The healthcare organizations have provided immense insights and professional aid to make this Apple health records epic better and more efficient. The biggest challenge is to keep these records safe in the cloud system so that any service provider related to this process can access them without any hassle. This PHR can be easily carried on the phone and can be shown to the entitled professionals for better and faster treatment response. The news and rumors of Apple to launch Health Records system have now surfaced and the consumers are hoping for a better day in the future.
Features of Apple health records app
This app will be the first of its kind that will maintain the FHIR standard of specifications fabricated by HL7. This particular smart phone application will carry personal information such as medications, allergies, present condition, ailments, immunization records, etc. In fact, the interoperability feature of this application will also allow the medical professionals to access the information and also to check electronic health records for lab results and other information. The Apple health records app is currently released as a beta version to find out the reaction of the users and its compatibility. All the information enlisted in this app will remain encrypted to keep it safe.
Apple announces Health Records platform and is currently developing a better platform for all the consumers that can be accessed by the entitled professionals as well. As per the plan, this information can be availed by the following professionals or organizations:
Data has long been a popular topic in healthcare and is even more so after this year’s HIMSS. The industry is buzzing about the joint CMS and ONC announcement, which proposes a framework to improve interoperability and support seamless and secure access of health information. The pressure is on for healthcare to tackle their data as the two organizations strive to provide patients with the ability to leverage personal information in various applications. And, this pressure will only increase as we look into the future, making it even more imperative that payers and providers address the issue now.
Look more closely, and you will see that with their recent announcement CMS and the ONC are focusing on healthcare organizations’ ability to manage data across the enterprise. Historically, healthcare has worked from siloed applications and data sources with light integration using interface engines. Recently, healthcare organizations have pinned their hopes on leveraging data effectively through huge investments in new EHR platforms. The reality, pointed out by government officials at HIMSS in Orlando, is that this still results in significant challenges for healthcare organizations to manage information across the data value chain.
Although not part of their proposed framework, CMS and the ONC point out the need for better patient mastering across data sources. Organizations hoped their investment in a centralized EHR platform would solve this but that has proven to not be the case. In addition to patient data, healthcare organizations face challenges in mastering physician data, which can have wide impact, including on value-based care initiatives. The joint proposal also highlights that the ability to push back accurate, cleansed data to source systems is critical.
Healthcare needs a unified approach
Using FHIR to stop data blocking and push the industry towards a standards-based approach will help, but it’s not sufficient for the data challenges facing healthcare organizations. In addition to tackling the issues pointed out at HIMSS, healthcare organizations must:
Health Level Seven International (HL7), announces the launch of the HL7 FHIR Accelerator Program. The program is based on a model piloted by the HL7 Argonaut Project and, more recently, the HL7 Da Vinci Project. The goal is to strengthen the FHIR (Fast Healthcare Interoperability Resources) standard and enhance market adoption through a programmatic approach available to myriad stakeholders.
“HL7 FHIR has achieved remarkable adoption on a global scale,” said Dr. Charles Jaffe, CEO of HL7. “An ever-growing community of implementers has emerged across a broad spectrum of health care, eager to participate in an agile onramp for FHIR adoption and implementation. The HL7 FHIR Accelerator Program provides the framework for that community to leverage the technical capability, management expertise and experience gained during the creation and growth of the Argonaut and Da Vinci Projects.”
Building on the success of current projects – Argonaut (provider-provider and provider-patient) and Da Vinci (payer-provider) – The CARIN Alliance has recently been approved as an HL7 FHIR accelerator project (payer-patient). The three projects are complementary initiatives.
“On behalf of the CARIN Alliance, its board and membership, we are grateful for the opportunity to work more closely with HL7 as part of the FHIR Accelerator Program as we work to develop additional FHIR implementation guides so consumers can get access to more of their health information,” said Ryan Howells, CARIN Alliance Project Manager and Principal at Leavitt Partners. “Consumers and their authorized caregivers are requesting more access to health care data with less friction to empower them to become more informed, shared decision-makers in the care they receive.”
The original concept behind accelerating HL7 FHIR began approximately four years ago with the advent of the Argonaut Project.
Guest post by Gavin Robertson, CTO and senior vice president, WhamTech.
As technology continues to permeate healthcare in different ways, it is becoming increasingly important for providers to have access to the data generated and retained by these technologies. With insurance providers, hospitals and clinics using a variety of electronic health records (EHRs), patient portals and databases, it can be difficult for all providers to have access to all relevant and most recently updated patient information. Differences among EHR vendors and systems make data access, sharing and interoperability nearly impossible.
Interoperability is a hot topic in healthcare today. Healthcare providers want to move beyond conventional Healthcare Information Exchanges (HIEs) that generally exist as single application to single application (P2P) data formats. The HL7 standard data model has helped a lot, but (i) it is too complex and extensive for full adoption, (ii) it is, typically, a specific relational or hierarchical implementation, requiring additional transformation, and (iii) there are a number of implementation variations.
Regardless of the improvements associated with the HL7 standard data model, the challenges facing interoperability remain; in that (a) multiple vendors have multiple ways to represent common data, (b) data may be required from more than one application and associated data sources, (c) poor data quality, (d) there may be no unifying view of data from one or more data sources, e.g., single patient view, and (e) there is no ability to write back to/update data sources.
HL7-based FHIR (Fast Health Interoperability Resources) APIs is a recent attempt to standardize access to data sources, but most vendor systems are nowhere close, as it is a different way of representing data from most vendor data schemas; i.e., object vs relational data representation. Also, some FHIR APIs need access to multiple tables in a single data source or in multiple data sources.
To implement FHIR APIs, one approach is to convert between the data source schemas (relational, hierarchical or flat) and the FHIR object model on-the-fly, but it does not address other shortcomings (poor data quality, no federation and lack of master data management (MDM)/single patient view). Another approach, which improves on just converting formats, is to copy and transform data into FHIR-friendly data stores and enable data services on top. However, this introduces additional problems, including latency, security, privacy, no interactivity; e.g., no write back/update to operational systems, additional storage and systems, and time and cost to implement.
Regardless of the approach, FHIR APIs open up interoperability and raise capabilities to new levels. New workflows can be developed and run using simple power end-user applications, such as BPM, reporting, BI and analytics tools. Examples include new smartphone app-driven BPM workflows running against FHIR API services, include write back/updates, on multiple legacy data sources in multiple organizations. Another example being hybrid cloud installations where multiple data sources are both on premise and in the cloud.