Tag: big data

Six Ways Technology Has Impacted The Healthcare Industry

Teal Stethoscope On Wooden Table

A few decades ago, we never would have imagined a world that looks the way it does today, and that’s all due to technology. Technology has significantly transformed multiple areas in our lives, and it continues to impact different sectors every day. 

One such sector that experienced massive transformation is the healthcare industry. With the global market valued at $281.1 billion in 2019 and expected to increase at a CAGR of 7 percent every year until 2027, it’s no doubt that the industry has experienced a boost due to various technological developments.

Today, patients can benefit from state-of-the-art diagnostic tools, groundbreaking treatment, and a wide range of minimally-invasive medical procedures that are less painful and result in faster healing. These developments are just the tip of the iceberg as technology has radically revolutionized the healthcare industry and significantly enhanced its operational efficiency. 

Hence, technology has impacted the healthcare industry massively. The following aspects further delineate its impact:

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Examples of Changes Big Data Has Brought To Medical Treatment

white building with data has a better idea text signage

By Adrian Johansen, freelance writer; @AdrianJohanse18.

Technology has always been at the forefront of improving our understanding of diseases, but the rise of big data has taken this to new heights. Big data in healthcare isn’t new, but it is worth discussing over and over again because it has not yet reached its full potential. No one even knows what its full potential looks like yet.

Even still, the application of big data in healthcare has now reached a point where it’s producing meaningful results not only for researchers but also for clinicians and patients.

Big data has provided changes to the way people in healthcare and research work, but what about the changes it’s provided to specific treatments? These changes are already here, and they’re indicative of both what’s to come and what’s possible for both individuals and patient populations.

What is Big Data in the Healthcare Context?

Big data is a broad concept with applications in a wide swath of fields. In the healthcare context, big data refers to the practice of collecting, analyzing, and using data from many different sources, including patient data, clinical data, consumer data, and physical data. In the past, it was possible to collect only a few types of data in smaller volumes because the tools needed to process and apply it were unavailable.

In this way, big data goes hand-in-hand with other technological developments, like machine learning and artificial intelligence (AI). Before machine learning, both clinical studies and applications were massively limited in terms of their scope: you could only handle a certain volume of data or a set variety. Veracity was also a problem with big data sets, which impacted the validity of studies.

Today, big data is a huge part of healthcare. You can find it in the creation of electronic health records (EHRs), pharmaceutical research, medical devices, medical imaging, and genomic sequencing. It differs from previous advances because it encompasses what data scientists call the 3Vs of Big Data: volume, velocity, and variety.

Big Data Reintroduces Old Treatments

Evidence-based medicine is at the core of modern practice. From diagnosis to treatment, physicians and specialists rely on an extensive foundation of research before making decisions. Medical big data has the ability to impact predictive modeling, clinical decisions, research, and public health. But it does so with greater precision: big data uses temporal stability of association. It leaves causal relationships and probability distributions behind.

Hypertension represents an ideal case study of the impact of big data on medicine. Despite the various effective medicines, including beta-blockers, the rates of uncontrolled hypertension in the general population are still very high. Scientists are using big data and machine learning to identify other drugs that may be working against beta-blockers to prevent the patient from gaining control of their blood pressure. One study identified proton pump inhibitors (PPIs) and HMG CO-A reductase inhibitors as drugs that weren’t previously considered to be antihypertensive but that actually improved success rates in hypertension treatment.

Without big data, it would be both time-consuming and expensive to rerun studies on these kinds of drugs. Moreover, there simply wouldn’t be enough data available to do it.

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Why Data Interoperability Is Critical In The Fight Against The Pandemic

By Daniel Cidon, CTO, NextGate.

Daniel Cidon

As COVID-19 closes in the on U.S., the need for longitudinal health data and interoperability have never been greater. Providers need access to the full picture of every patient they treat, and epidemiologists need to consolidate data from multiple sources to track the spread of the disease and determine where more aggressive containment strategies need to be employed.

For many organizations already overwhelmed, fragmented systems lead to an infrastructure bottleneck, resulting in degraded data quality, gaps in care coordination, medical errors and burdensome workflows. Lack of comprehensive medical data impairs a provider’s ability to know how many people have the virus, the geographical location of confirmed cases, and the effectiveness of treatment.

Even as capacity restrictions force organizations to work without barriers—via drive-thru screenings, make-shift tents or by way of telehealth—real-time access to data can help streamline care management, whether fast tracking admissions or empowering patients at home through online portals.

Here are just five ways data interoperability plays a pivotal role in addressing the epidemic:

Coordination of Care: COVID-19 provides a sobering reminder of just how dire an integrated, scalable and interoperable healthcare infrastructure is. Coordination among first responders, public health officials, labs, acute and post-acute facilities will be critical to efficiently deal with the explosion of cases. Insurers will also be a key player of the care coordination team as to not slow down or hold up prior authorizations and patient discharges. Access to information about hospitalizations and test results among healthcare participants will be vital for enhanced continuity of care across settings and transitions. Real-time data afforded by interoperability bypasses the need for phone calls and faxes, which create delays and information inaccuracies.

Patient Identification: A complete view of one’s medical history can be a matter of life or death in the face of COVID-19. Bringing disparate medical records together into a cohesive story enables those on the frontlines insight into an individual’s pre-existing medical conditions, medications, allergies, etc. to make the most informed decisions under insurmountable circumstances. Patient demographics and data standardization play a huge role. Accurate patient identification ensures data about an individual is correctly linked, updated and shared, for improved clinical decision-making and enhanced care quality and safety. As health officials look to track and predict the spread of the virus. A complete view of the patient population can only be done with a firm understanding of the patient’s identity, and the key relationships the patient has to their next of kin and to their providers of care.

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Healthcare Tech and Its Help In Diagnosing Patients

By Brooke Faulkner, freelance writer; @faulknercreek.

Up to a fifth of patients with serious conditions are first misdiagnosed, and that leaves tremendous consequences. With the help of healthcare technology, doctors are able to diagnosis patients more effectively and easier. For example, migrating patient data from paper to online, known as electronic health records (EHRs), has greatly aided the medical world. Technology, especially using artificial intelligence and predictive analytics, has enabled doctors to make faster, more accurate diagnoses, and thus provide better care.

The volume of big data

Duquesne University estimated there to be 150 exabytes of healthcare data collected in 2011. Four years later, they reported about 83 percent of doctors had transitioned from using paper to electronic records. By now, with the ubiquity of the cloud, these numbers have assuredly gone up.

Massive amounts of data make predictive analytics possible, as trends can be spotted and analyzed. By spotting patterns, diagnosis of a disease becomes easier even for doctors unfamiliar with a specific disease or symptom. Uploading symptoms allows a computer to compare records and identify symptoms comorbid of other problems. This allows even specialized doctors to recognize issues outside of their field. Medical mistakes lead to the death of some 440,000 people each year; while misdiagnosis is only a part of this number, correct diagnosis and treatment will reduce it.

Big data can even be collected in the form of PDFs as part of telemedicine. A doctor can send PDFs to patients as part of a poll or survey or simply to collect symptom information from the patient. From there, data entered in the PDF can be collected and analyzed, generating patient data or feedback for the doctor.

Google flu trends

Google ran what can best be called an experiment from 2008 to 2014. Using artificial intelligence, the search engine recorded flu-related searches in an attempt to predict the severity of an outbreak, as well as the affected geographical area.

It was a flawed model, and tried to use big data as a replacement, rather than a supplement, for traditional data collection and analysis. It completely missed a flu outbreak in 2013, the data off by a massive 140 percent, and Google Flu Trends ended its public version in 2014. The algorithm monitoring flu-related search terms was simply not sophisticated enough to provide accurate results. While new data is no longer available to the public, historical data remains available to the Centers for Disease Control and other research groups. It’s possible that once the algorithm and predictive analysis is capable, the program will continue.

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Sharing Too Much? A Tale of Privacy, Big Data and Sleep Technologies

By Brooke Faulkner, freelance writer, @faulknercreek.

The proliferation of wearable mobile-connected devices has done a lot of good for people trying to lead healthier lives. People are able to gather data about their sleep to help them get better rest, track insomnia, stress, and exercise, and keep up to date on their own daily routines and health.

Many of the devices exist not just as trackers but as ways for people to motivate themselves to exercise more, go to bed at more regular times, and other little things that slip by in the daily grind.

Specialists can access information far more quickly and easily to help us with medical problems. With the way technology is advancing, you can now even also use online apps to calculate sleeping patterns and wake up times.

Healthcare Data in the Modern Age

Sleep is absolutely related to health. Many health issues affect our sleep or are caused by issues with sleeping. A number of medical professionals are interested in how, when, and for how long we sleep. These days, that information is stored in digital medical records, which have a number of advantages. Specialists can access information far more quickly and easily to help us with medical problems.

There are, however, disadvantages to medical records being easily accessible and easily updatable. Privacy and security have become major concerns for healthcare providers, as the records contain our most sensitive information, which proves highly valuable to hackers.

Official medical records, however, are just the tip of the iceberg. We use the internet not only as a go-to for advice about medical conditions, but as a method to voluntarily record all sorts of data about us. Recording, storing, and tracking sleep data on our personal devices gives us a lot of power to “do it yourself” when it comes to preventative health and tracking changes in our sleep patterns. This ease of use, however, comes with a cost. It’s not all about sinister hackers, either; that data can be used in all sorts of ways that are, while not outright damaging, at least partially invasive.

Wearables, Bluetooth and Data

Tech companies are working on more advanced ways to use high-tech devices to track our sleep. These include wearables and even devices that don’t need to be attached to the body. The amount of information gathered, and its accuracy, varies greatly by device. The Bluetooth connectivity and the ability to store, track, and share the data the devices collect are parts of their appeal.

The number and type of people who benefit from this information is vast. Sleep disorders are a common side effect of other medical disorders, and managing sleep is an important part of living a healthy lifestyle, especially for people who are at greater risk for certain conditions.

You don’t need to have or be at risk for medical complications to make use of sleep data, however. There are plenty of careers in the U.S. which require people to work long or unconventional hours. Night shifts and long shifts, such as those worked by nurses, can cause havoc with the circadian rhythms that regulate our sleep. This can create complications for otherwise completely healthy people. Being able to self-regulate with the help of wearable devices is a great advantage.

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The Benefits of Automation in Hospital Management

To face and handle several challenges along the way, the healthcare industry is looking towards the IT sector for the best tools and equipment. As demands for better treatment and diagnostic procedures continue to rise, it is best for healthcare organizations, especially hospitals, to upgrade their infrastructure and deliver the best results to this end.

Big data, demands for better therapeutic methods, as well as increasing management-side complexity are challenges that clinics and hospitals will have to address. Automation is nothing new in this respect, but it demands wider adaptation among healthcare organizations that struggle with outdated equipment and lackluster patient information management.

With that being said, it is imperative for these organizations to look into hospital management systems and how they can help streamline regular and complex operations.

Automation saves costs

Automation points the way to the future of healthcare technology. One thing’s for sure, there will be a high dependence on automated systems for such areas as healthcare denial management and revenue accounting. Through an effective software product, a hospital can make significant cuts to operational costs, enabling the savings to be channeled towards the development of better facilities and the procurement of advanced equipment.

Automation lightens the workload

Hospital staff have a lot of things on their plates. More often than not, they will have to handle routine tasks such as validating patient data and organizing a large bulk of information. Using intelligent solutions to everyday responsibilities enables you to lighten the workload on your staff so they can focus on more important functions.

Automation streamlines medical billing

Another high point of using effective hospital management software is that it allows an organization to make proper computations for their patients. This has always been a challenge that hospitals need to endure way back when accounting software was not as sophisticated as it is now. But with recent innovations in modern tech, it is possible for hospitals to reduce the amount of paperwork in accounting and to bill their patients without the possibility of a dispute.

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Four Predictions for Healthcare IT in 2018

Guest post by Steve Elder, director of communications, STANLEY Healthcare.

Steven Elder
Steve Elder

The healthcare industry is in a period of great uncertainty, with major questions looming around how regulations, standards and reimbursements – particularly regarding care quality and interoperability – will be changing for hospitals in the coming year. One thing is clear though: In order to provide the efficient and high-quality care needed to meet patient expectations, hospitals need to focus on the intelligent application of new technologies. Here are four trends that will influence healthcare IT in 2018:

The opioid epidemic will trigger growth in investments around patient and staff safety

The growing opioid epidemic now causes nearly 100 deaths each day, and is projected to cause 500,000 deaths over the next decade, primarily due to overdoses. That is not only putting pressure on hospitals to reevaluate how they use opioid medications and monitor patients once back in the community, but it is also forcing them to address the physical safety of staff and patients. This is because the opioid epidemic has led to an increase in violent crimes in healthcare facilities. Emergency departments in particular are under heavy strain, with more patients presenting with addiction symptoms, compounding wait times and leading to more patient disputes. Hospitals will have to invest significantly more in technologies to protect staff and patients, such as patient monitoring solutions and staff duress systems to prevent potentially dangerous patients from harming themselves or others.

 Big data advancements will pave the way for the rise of predictive and prescriptive analytics

Regardless of how the major causes of uncertainty affecting the healthcare industry – such as the future of the Affordable Care Act – resolve themselves, it is certain that there will be no return to the pre-ACA era. As healthcare industry writer and consultant Edgar Wilson has pointed out in the context of primary care, the expansion of insurance coverage did not magically create more capacity. It challenged hospitals to find new ways to serve more patients, more personally, without adding cost. Hospitals will continue to look for practical ways to improve their efficiency by leveraging data to better predict patient care requirements, and demand for medications and equipment needs. The benefits of these predictive analytics capabilities are enormous.

According to a February 2017 report by the Society of Actuaries, 93 percent of healthcare providers said predictive analytics is important to the future of their business, and 57 percent believe predictive analytics will save their organization 15 percent or more over the next five years. In addition to predictive analytics, prescriptive analytics will have a growing impact. Ongoing advancements in the collection, aggregation and analysis of data will provide hospitals with greater operational insights, enabling them to optimize staffing levels and other aspects of operations while enabling staff members to deliver more effective, targeted care.

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3 Ways Big Data and IoT Can Improve Our Health

Guest post by Alexandra Roden, content editor, Connexica.

Alexandra Roden
Alexandra Roden

Just a few years ago, big data and the Internet of Things (IoT) were terms generally unheard of. This year they continue to revolutionize technology and the ways in which we acquire and process data, but what do they mean for the healthcare industry?

Xenon Health describe IoT as “a phenomenon through which the operational aspects of the physical world become increasingly integrated with digital platforms, enabling information to move seamlessly toward the computational resources that are able to make sense of it.” Essentially, IoT goes hand-in-hand with the mobile age and the diversity of data that is currently being retrieved from agile and mobile locations.

Big data is a related concept – it addresses the ever-increasing amounts of data that are created every second of every day and recognizes that these figures will only continue to grow. For example, in the “social media minute” every single minute there are 277,000 tweets are sent, Whatsapp users share 347,222 photos and Google receives more than 4,000,000 search queries. These figures are remarkable even for those of us caught up in the social media hype, and most shocking of all is the realization that the global Internet population now represents 2.4 billion people. That’s a lot of people creating a lot of data – the question now is how we can utilize this data in a meaningful way.

IoT has revolutionized many industries and will continue to do so in the foreseeable future, but what about healthcare? Organisations within this industry tend to adopt new technologies slowly, relying upon solid evidence and demonstrable impact and efficiency before committing to any such change. The shift towards IoT is, however, beginning to take place, and increasing amounts of available patient data are beginning to inform decision making processes within this sector.

What will this mean for the future of our health?

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Collaboration of Data Key for Healthcare Interoperability

By Darin M. Vercillo, MD, chief medical officer and co-founder, Central Logic.

 Darin M. Vercillo, MD
Darin M. Vercillo, MD

Healthcare has been changing rapidly for the last 60 years and advances have now reached record speed, including in the realm of data intelligence. In trying to keep pace as well as to protect and advance their own businesses, many processes and systems have understandably been organized into silos.  That era must come to a close.

Care coordination teams need rich collaboration of data and must now be connected.  Hospitals, clinics, home health care workers, primary care physicians, vendors, and others must speak with each other, in the same language, and completely share patient data with an open, collaborative attitude. The industry is all abuzz with this uncharted territory called interoperability. It is clear that data warehouses, now bursting with valuable information, must be streamlined for three very simple reasons: patient safety, cost-effective healthcare delivery and overall population health management. A happy byproduct when data intelligence becomes actionable and systems work collaboratively is a financial benefit, but as a physician, I believe excellent patient care always wins the day, and should be the driving factor.

Reducing Re-admissions

Consider that about one-fifth of Medicare beneficiaries are re-admitted within 30 days of discharge and upwards of one-third are re-admitted within 90 days. One study by the American College of Physicians showed that 20 percent of patients have a complication within three weeks of leaving the hospital, more than half of which could have been prevented. In all, Medicare spends $26 billion annually on re-admissions, $17 billion of which could be “preventable.”

At the risk of this being looked at as “just a financial issue,” consider also that hospitalization is generally a marker for severe illness. Our goal is a healthier population. As we (patients and providers) succeed collectively with hospital treatment and post-acute care, then re-admissions will naturally decrease, and patients will live healthier, more satisfied, lives. Ultimately, this is our goal.

Appropriate, timely sharing of vital patient information will not only address re-admission rates that have clearly become egregious, but improved collaboration of data needs to happen to better inform decision making at the point of care. Without a keen eye to patient safety and success, it is too easy for details to slip through the cracks. All too often, history has demonstrated that hand-off points are the riskiest for failures in patient care.

Nearly everyone has a story where the current system has failed patients — just ask Jennifer Holmes, our CEO. Her father’s healthcare team made an error in medication that ultimately cost him his life. Similar medication errors and decreased duplicate testing can be avoided when a patient’s entire care coordination team has visibility into the data – all the data – to improve care efficiencies and diagnoses.

But all this sharing and playing nice in the sandbox is easier said than done.

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Releasing the Power of Big Data through Proper De-Identification

Guest post by Lucy Doyle, Ph.D., vice president, data protection, information security and risk management, McKesson, and Karen Smith, J.D.,CHC, senior director, privacy and data protection, McKesson.

Today there are opportunities and initiatives to use big data to improve patient care, reduce costs and optimize performance, but there are challenges that must be met. Providers still have disparate systems, non-standard data, interoperability issues and legacy data silos, as well as the implementation of newer technologies. High data quality is critical, especially since the information may be used to support healthcare operations and patient care. The integration of privacy and security controls to support safe data handling practices is paramount.

Meeting these challenges will require continued implementation of data standards, processes, and policies across the industry. Data protection and accurate applications of de-identification methods are needed.

Empowering Data Through Proper De-Identification

Healthcare privacy and security professionals field requests to use patient data for a variety of use cases, including research, marketing, outcomes analysis and analytics for industry stakeholders. The HIPAA Privacy Rule established standards to protect individuals’ individually identifiable health information by requiring safeguards to shield the information and by setting limits and conditions on the uses and disclosures that may be made. It also provided two methods to de-identify data, providing a means to free valuable de-identified patient level information for a variety of important uses.

Depending on the methodology used and how it is applied, de-identification enables quality data that is highly useable, making it a valuable asset to the organization. One of the HIPAA- approved methods to de-identify data is the Safe Harbor Method. This method requires removal of 18 specified identifiers, protected health information, related to the individual or their relatives, employers or household members. The 18th element requires removal of any other unique characteristic or code that could lead to identifying an individual who is the subject of the information. To determine that the Safe Harbor criteria has been met, while appearing to be fairly straightforward and to be done properly, the process requires a thorough understanding of how to address certain components, which can be quite complex.

The second de-identification method is the expert method. This involves using a highly skilled specialist who utilizes statistical and scientific principles and methods to determine the risk of re-identification in rendering information not individually identifiable.

We need to encourage and support educational initiatives within our industry so more individuals become proficient in these complex techniques. At McKesson, we are educating our business units so employees can better understand and embrace de-identification and the value it can provide. This training gives them a basic understanding of how to identify and manage risks as well as how to ensure they are getting quality content.

Embracing Social Media and New and Improved Technologies

One of the challenges we face today in de-identifying data is adapting our mindset and methodologies to incorporate new emerging technologies and the adoption of social media. It is crucial to understand how the released data could potentially be exposed by being combined with other available data. New standards are needed.

Closing Thoughts

While de-identifying data can be challenging and complex, the task is made easier when we remember and adhere to our core directive to safeguard data. With this in mind incorporating new technologies is part of an ongoing process of review.

When done properly, de-identification enables high quality, usable data, particularly when the expert method is used. De-identification should not be viewed as an obstacle to data usage, but rather as a powerful enabler that opens the door to a wealth of valuable information.

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