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.
Cardiovascular diseases remain the number one killer of people in the world, resulting in 31 percent of all global deaths (17.9 million per year), and are the most expensive condition to treat. However, AI and machine learning technologies are being developed to make care pathways, treatment and real-time visualization of cardiac anomalies and subsequent therapy more effective. Artificial intelligence (AI) and machine learning capabilities may provide numerous advantages over traditional analytics and clinical decision-making techniques, and cardiology is likely to benefit tremendously from these advancements as they mature.
“As machine learning-based algorithms become more precise and accurate by interacting with data and programmed information, these technologies will allow care teams to gain unprecedented insights into diagnostics, care processes, treatment variability and patient outcomes, especially in regard to cardiac care,” said Stuart Long, CEO of InfoBionic, the leading digital health company that created the MoMe Kardia remote cardiac monitoring platform.
“AI algorithm-based cardiac devices can procure tremendous amounts of data, providing for the ability to match up what physicians are seeing to long-term patterns and possibly detect subtle improvements that can impact care,” noted Long.
Leveraging AI for clinical decision support, risk scoring and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis. Powering new tools and systems can help make clinicians more aware of nuances, more efficient when delivering care, and more likely to curb a patient’s developing health problems.
AI is ushering in new clinical quality and breakthroughs in patient care. For example, at the Cleveland Clinic, a customized algorithm developed by clinicians analyzes data, including blood pressure, heart rate and oxygen saturation levels, to flag the patients that are at highest risk of deterioration. The ultimate goal is to provide front-line clinicians notice of serious cardiac events before they happen. Moreover, the precision now possible with cardiovascular imaging, combined with “big data” from the electronic health record and pathology, is likely to lead to tremendous cases of cardiac disease management and personalized therapy.
Healthcare consulting firm, Frost & Sullivan, projects a 40 percent growth rate for AI in healthcare between 2016 and 2021, and said AI has the potential to improve outcomes by as much as 40 percent, while reducing the costs of treatment by as much as 50 percent.
By George Mathew, MD, chief medical officer, North America, DXC Technology.
Patients, like all consumers, are more digitally aware and connected than ever before, as they continue to embrace the latest mobile devices and wearables. These devices, as well as the increasing availability of information on health management, have made patients more engaged participants in managing their own health and wellness.
As a result, they demand timely access to their own health information and expect care services that are personalized and convenient. They also want to use consumer-friendly digital tools to engage with their clinical records, lab results, medications and treatment plans.
However, many health organizations are still evolving their approach to meet this challenge. Existing systems of record in healthcare are often siloed, making it difficult to share actionable patient information across the continuum to accelerate service delivery and improve outcomes. The solution lies in implementing next-generation digital health platforms to integrate sources of historical clinical and wellness data to derive insights that drive more engaging patient experiences, better outcomes and lower costs.
Bridging the Information Gap
Integrating data sources across healthcare segments and aggregating them into a single digital-patient record, empowers patients and providers to make better healthcare choices and improve quality of care.
Rather than searching and clicking across multiple systems, an integrated digital patient-care platform creates a “single source of truth” to give patients and their providers quick and easy access to real-time, context-specific information for timely decisions. Benefits include the following:
Providers can optimize clinical operations, with results that include streamlined processes, reduced patient admissions, shorter hospital stays and, ultimately, improved quality of care for patients.
Patients may obtain a full view of their complete health journey and access relevant education and medication information — instead of having to wait for follow-up visits to see and discuss their results.
Patient engagement can also be improved through secure patient messaging capability, the ability for providers to receive patient experience feedback, and deployment of intelligent virtual assistants across a range of mobile devices to create a connected healthcare experience.
Additionally, when healthcare staff have access to the most up-to-date data, they can ensure the right materials are in the right place, reducing material waste and minimizing patient wait times. Furthermore, integrating clinical and wellness systems can help providers efficiently collect population health data to maximize health outcomes through early interventions.
Vital, the AI-powered software increasing productivity and improving patient health in hospital emergency rooms, today announces a $5.2 million Seed round led by First Round Capital and Threshold Ventures (formerly DFJ Venture). Vital uses artificial intelligence (AI) and natural language processing (NLP) to triage patients before they see a doctor, making it easier and faster for providers to coordinate care and prioritize patients.
Raised to help grow the Vital team of engineers and data scientists, and to bring its secure, cloud-based software to emergency rooms across the United States, the round also includes Bragiel Brothers, Meridian Street Capital, Refactor Capital and SV Angel, with angel investment from Vivek Garipalli, CEO of CloverHealth; and Nat Turner and Zach Weinberg, founders of Flatiron Health. Josh Kopelman, founder and partner at First Round Capital, will join Vital’s board of directors.
“The HITECH* Act was well-intentioned, but now hospitals rely on outdated, slow and inefficient software – and nowhere is it more painful than in the emergency room,” said Vital founder and CEO Aaron Patzer in a statement. “Doctors and nurses often put more time into paperwork and data entry than patient care. Vital uses smart, easy tech to reverse that, cutting wait times in half, reducing provider burnout and saving hospitals millions of dollars.”
Patzer brings capital to Vital from his success with Mint.com, which transformed bank data into an easy consumer product. The decision to take on an even higher-stakes, more regulated industry came after seeing firsthand the antiquated software hospitals use. Teaming up with Justin Schrager, doctor of emergency medicine at Emory University Hospital, Patzer invested $1 million and two years of peer-reviewed academic study, technical research and development to create Vital.
“Vital successfully built software with a modern, no-training-required interface, while also meeting HIPAA compliance. It’s what people expect from consumer software, but rarely see in healthcare,” said Kopelman. “Turning massive amounts of complex and regulated data into clean, easy products is what Mint.com did for money, and we’re proud to back a solution that’ll do the same in life and death situations.”
*The ACA’s Health Information Technology for Economic and Clinical Health
Recent research was published by the Washington Post about malware that was created to disrupt medical imaging equipment and networks. This is yet another wake-up call for the healthcare industry that been underinvesting in security for the last decade. Quite simply, there is a misconception that hospitals’ internal networks are a safe harbor from external cyberattacks. This is despite the fact that the real-world data has repeatedly shown that healthcare is one of the top industries under attack for the last five years. While previous attacks mainly focused on stealing personal health information, this research demonstrates how serious or even deadly an attack to healthcare can be.
There are a few reasons why cyberattacks in healthcare today can have devastating consequences.
Medical device vulnerabilities
Many medical devices inside hospitals are running decade old operating systems and applications that have many well-known vulnerabilities. In fact, it may be a surprise to many that the vast majority of imaging systems run on Windows OS. Further, recent Zingbox research shows that today, 1 out of 4 imaging systems run on OSes that are no longer supported. By next year, 85% of imaging systems are expected to run on End-of-Lifed OSes as Microsoft terminates support for some of their popular Windows OSes.
To make matters worse, most medical device manufacturers lack strong in-house cybersecurity expertise. While their expertise lies in device reliability and accuracy, which continue to be top requirements for connected medical devices, the lack of cybersecurity expertise puts the device reliability and accuracy into question. The lack of cyber-specific expertise also limits manufacturers’ ability to “bake in” cybersecurity measures on the device.
One might think that patches and upgrades are the answer. Unfortunately, no. FDA certification and other requirements pose significant hurdles for manufacturers to apply patches or upgrades to devices already deployed at hospitals.
Tools designed for IoT
Many hospitals lack the tools to monitor life-critical devices with 100% assurance of uninterrupted service and care. Such tools must be completely transparent to the device and in no way interfere or hamper its operation. Yet, organizations continue to rely on traditional IT security solutions for IoT. Such network security tools like firewalls and antiviruses result in security gaps that hackers can easily exploit.
Vulnerabilities that stem from inadequate IoT security tools:
Most network security solutions often cannot discern a PC from a CT scanner, whereas such a distinction is critical for cybersecurity.
CT scanner’s communication is almost never encrypted, device access doesn’t require basic authentication, and given the mobility of typical CT scanners, the devices can be connected to any internal network, according to Zingbox’s research findings.
Connecting a device to any network breaks the basic micro-segmentation policies IT teams have been encouraged to deploy for cybersecurity.
By Bentley Raynott, healthcare technology analyst, Tatvasoft.
With the advent of new technologies, the entire healthcare industry is gearing up to adopt new initiatives. Marked with different growth patterns and trends that could vary within its different sub-domains, the industry seems to have witnessed a couple of contradicted situations with inconsistent stagnation in others. The following post sums up certain trends to watch out in the development and data-driven technologies to take into account.
Over the past few years, artificial intelligence, machine learning, big data technologies have shown a prolific growth like never before. For example, IBM Watson — an AI-based system seems to have enhanced core management, accelerated drug discovery, matched patients with clinical trials, and fulfill other tasks. It is considered as one of those systems that have aided several medical institutions saving a great deal of time and money in the future. It is assumed that in 2019 and all the upcoming years, AI will become more advanced and will be carrying a wider range of tasks without human monitoring. Here are some predictions of AI trends in healthcare.
Having a patient-centric approach has become a norm these days. A unique radical change in the set of expectations that a consumer has today. Do you feel that front end challenges has the potential to suffice the need for consumerism especially in regards to the diagnostic aspect of patient care? Well, several diagnostic entities are found embracing the superior methods through which they can stay ahead in the value-driven system.
It may interest you to know that some of the major pharma players are seen investing in the consumer genetics field for the development of novel pharmaceutical products. As a result, GlaxoSmithKline entered into a four-year collaboration with 23andMe; one of the leading players in the genetic testing market; for drug discovery through human genetics.
Being in the healthcare sector, clinical decision-making is expected to advance in order to provide simple, standardized, effective and efficient across the care spectrum focusing on prevention, illness, and chronic care wellness, by virtue of a comprehensive strategy such as consumer genomics and precision medicine.
Gene editing technologies will advance
This technology, especially in the diagnostic platform, are making to change the face of disease detection, bio-sensing, and diagnostics in the years and many more. Moreover, it can also prove equally viable in the field of agriculture, biomanufacturing and forensics.
A foundational research roadmap for artificial intelligence (AI) in medical imaging was published this week in the journal Radiology. The report was based on outcomes from a workshop to explore the future of AI in medical imaging, featuring experts in medical imaging, and hosted at the National Institutes of Health in Bethesda, Maryland. The workshop was co-sponsored by the National Institute of Biomedical Imaging and Bioengineering, the Radiological Society of North America, the American College of Radiology, and the Academy for Radiology and Biomedical Imaging Research.
The collaborative report underscores the commitment by standards bodies, professional societies, governmental agencies, and private industry to work together to accomplish a set of shared goals in service of patients, who stand to benefit from the potential of AI to bring about innovative imaging technologies.
The report describes innovations that would help to produce more publicly available, validated and reusable data sets against which to evaluate new algorithms and techniques, noting that to be useful for machine learning these data sets require methods to rapidly create labeled or annotated imaging data. The roadmap of priorities for AI in medical imaging research includes:
new image reconstruction methods that efficiently produce images suitable for human interpretation from source data,
automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting,
new machine learning methods for clinical imaging data, such as tailored, pre-trained model architectures, and distributed machine learning methods,
machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence), and
validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets.
Langlotz, CP, et al. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. April 16, 2019.
Co-authors of the report with Curtis P. Langlotz were Bibb Allen, M.D.; Bradley J. Erickson, M.D., Ph.D.; Jayashree Kalpathy-Cramer, Ph.D.; Keith Bigelow, B.A.; Tessa S. Cook, M.D., Ph.D.; Adam E. Flanders, M.D.; Matthew P. Lungren, M.D., M.P.H.; David S. Mendelson, M.D.; Jeffrey D. Rudie, M.D., Ph.D.; Ge Wang, Ph.D.; and Krishna Kandarpa, M.D., Ph.D.
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.
Healthcare technology is advancing quickly and this is precisely why executives need to be aware of all new technologies that can make their healthcare organisation more efficient and more impactful. This may seem difficult – staying on top of things and implementing new technologies always is, but it brings immense benefits and great results. While many technology advancements come with all that fame that is often not necessary, it can make patient satisfaction better. It can also improve cost savings and this is really important for the future of your organisation.
So, in this spirit, here are some of the most amazing tech advancements that can help your healthcare organisation become better and take another step towards the future.
Blockchain can make interoperability ai reality. You can solve many problems between healthcare organisations and it’s a solution that healthcare industry has been looking for for many years. It can decentralize the record systems and have multiple locations that can be shared with more stakeholders. This will help the healthcare system immensely and it can operate within different stakeholders in the healthcare systems. Instead of having a single client database, you can include both clinical and financial data on one server and in an independent, transparent database.
“Blockchain technology can share data in a safe system and put the clients and their needs at the center of the attention. Still, healthcare industry is a decade away from implementing blockchain in a meaningful way,”says Ingrid Fulton, a tech editor at Draft beyond and ResearchPapersUK.
Artificial intelligence can help with better oncology. Veterans Affairs is helping with this as a part of their precision oncology program which supports patients that have stage 4 cancer and that have tried all other methods of getting better. They are using AI to help use cancer data in the treatment of these patients. They are also veteran.
They treat more than 3.5 percent of patients in the US and this is the largest group of patients with cancer within any healthcare groups. This includes veterans from rural areas where it has been hard for them to implement better technology, especially something of this value.
Now more than ever, the healthcare industry is leveraging new technologies to provide patients with improved, innovative care. The innovation attracting the most buzz in the healthcare industry today is artificial intelligence (AI). However, despite the ongoing hype of robots and algorithms as industry game-changers, results to date from early applications of AI in healthcare have fallen short of realizing dreams of sweeping improvements.
IBM’s Watson is an excellent example of how these improvements “in healthcare” will require a more step-by-step approach and may take longer to achieve than initially thought. In 2011, Watson garnered worldwide attention by winning a game of Jeopardy against two of the show’s greatest champions. Within healthcare, Watson’s win gave rise to hope that AI was on the precipice of full-scale deployment that would transform the industry and dramatically improve patient outcomes.
For several reasons, that hasn’t quite happened yet, and Watson has found it challenging to deliver improved patient outcomes. While those critical of AI have been quick to jump on these struggles, it’s crucial to acknowledge that Watson suffers from several common obstacles faced by AI in healthcare. These include the lack of high-quality data that can be used to train an algorithm, the low number of available training cases, implicit bias, and the differences in guidelines between the U.S. and other countries.
However, as the industry collectively works to address these issues, I envision three major areas where AI will soon transform personalized medicine.
Individualizing the patient-clinician relationship
Clinicians are already equipping themselves to better serve their patients with the predictive and organizational benefits of AI. This technology will move the field away from a “one-size fits all” approach and make the clinician-patient relationship more individualized, fostering trust.
This would be no small feat for improving the patient-clinician relationship, especially for those suffering from chronic conditions. A study by West Corporation in 2018 found that only 12 percent of chronic condition patients feel strongly that their provider is doing a good job of delivering information specific to their needs and condition.
When a clinician provides patients with unique, individualized solutions, patients feel empowered and are more comfortable speaking up throughout the treatment process. When a patient is comfortable enough to report symptoms, no matter how trivial they may seem, personalized medicine thrives.
With the help of AI, clinicians can search extensive amounts of information to find the causes of patient-reported symptoms and alter patient care accordingly. These improvements can be referenced by other clinicians and lead to large-scale medical breakthroughs.