Tag: healthcare data

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.

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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Healthcare Data Is Bigger Than You Think

By Chris Astle, CEO, QSR International.

Chris Astle
Chris Astle

Data has been regarded as the new, shiny object in every industry for the last several years—the secret key to all your unanswered questions. The healthcare industry has been no stranger to this language, but has not had as much opportunity to put data to use as other industries. With fast-paced, “ready for anything” schedules, hospitals, EMTs and private practices have had to leave data analysis to the researchers.

A 2017 Bankrate survey found that one in four Americans do not seek medical care when they need it. The survey results cite cost as the main reason for this. While healthcare providers cannot control insurance coverage and healthcare legislature, they can control the experience patients have when they seek care. A patient is much more likely to return for an annual check-up or seek medical care when sick if they hold healthcare to be positive and important. By giving patients the chance to voice their opinion, to feel heard and capturing and analyzing this powerful data, you will create a positive atmosphere. This will then lead to things like patient retention and positive online reviews.

With technology ever advancing, data analysis is simplifying. It is becoming something that a person with no research experience can do and find benefit. Take, for example, open-ended survey analysis software. Most data software analyzes the quantitative or number-based data. This includes the numerical details that you gather like a patient’s vitals. Data is much more than that. Imagine being able to analyze not only quantitative data, but qualitative too, including things like open-ended answers. This opens the possibility to hear directly from patients, doctors, and nurses, not only to better customer service, but importantly, care.

Mixed-question surveys provide health practitioners a simple way to gain new insights. Have patients answer a few questions through your medical portal or while at your office that ask basic questions like, “Rate your experience,” and, “How likely are you to recommend our practice to others?” But do not feel restricted by these types of questions, there is much more to learn beyond whether someone has had a generally good or bad experience. Ask them why. Ask for suggestions of how to improve care. Ask patients to describe their symptoms or the side effects of their medication. Each answer becomes part of a data set that you can analyze and cross-examine to give you new ideas and findings, and contribute to providing a higher standard of care for patients.

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Health IT’s Responsibility to Produce Actionable Healthcare Data in 2016

Guest post by LeRoy E. Jones, chief executive officer, GSI Health, LLC.

The Care Coordination LeadersThe health IT revolution is here and 2016 will be the year that actionable data brings it full circle.

Opportunities to achieve meaningful use with electronic health records (EHRs) are available and many healthcare organizations have already realized elevated care coordination with healthcare IT. However, improved care coordination is only a small piece of HIT’s full potential to produce a higher level synthesis of information that delivers actionable data to clinicians. As the healthcare industry transitions to a value-based model in which organizations are compensated not for services performed but for keeping patients and populations well, achieving a higher level of operational efficiency is what patient care requires and what executives expect to receive from their EHR investment. This approach emphasizes outcomes and value rather than procedures and fees, incentivizing providers to improve efficiency by better managing their populations. Garnering actionable insights for frontline clinicians through an evolved EHR framework is the unified responsibility of EHR providers, IT professionals and care coordination managers – and a task that will monopolize HIT in 2016.

The data void in historical EHR concepts
Traditionally, care has been based on the “inside the four walls” EHR, which means insights are derived from limited data, and next steps are determined by what the patient’s problem is today or what they choose to communicate to their caregiver. If outside information is available from clinical and claims data, it is sparse and often inaccessible to the caregiver. This presents an unavoidable need to make clinical information actionable by readily transforming operational and care data that’s housed in care management tools into usable insights for care delivery and improvement. Likewise, when care management tools are armed with indicators of care gaps, they can do a better job at highlighting those patients during the care process, and feeding care activities to analytics appropriately tagged with metadata or other enhanced information to enrich further analysis.

Filling the gaps to achieve actionable data
To deliver actionable data in a clinical context, HIT platform advancements must integrate and analyze data from across the community—including medical, behavioral, and social information—to provide the big picture of patient and population health. Further, the operational information about moving a patient through the care process (e.g., outreach, education, arranging a ride, etc.) is vital to tuning care delivery as a holistic system rather than just optimizing the points of care alone. This innovative approach consolidates diverse and fragmented data in a single comprehensive care plan, with meaningful insights that empowers the full spectrum of care, from clinical providers (e.g., physicians, nurses, behavioral health professionals, staff) to non-clinical providers (e.g., care managers, case managers, social workers), to patients and their caregivers. Armed with granular patient and population insights that span the continuum, care teams are able to proactively address gaps in patient care, allocate scarce resources, and strategically identify at-risk patients in time for cost-effective interventions. This transition also requires altering the way underlying data concepts are represented by elevating EHR infrastructures and technical standards to accommodate a high-level synthesis of information.

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Interpreting Healthcare Data: Start with a Good Denominator

Michael Barbouche
Michael Barbouche

Guest post by Michael Barbouche, founder and CEO, Forward Health Group.

As clearly identified in the PCAST Report on Health Information Technology (2011), and as echoed in the recent GAO report Electronic Health Record Programs — Participation Has Increased, but Action Needed to Achieve Goals Including Improved Quality of Care (2014), healthcare continues to have a data problem. The country has invested significantly to advance EHR adoption.

In simpler terms, healthcare data is messy and makes for building of accurate, actionable metadata a problem. It’s clear that the next generation of standards that are being developed by the numerous committees and acronyms and professional societies tackling measure development, harmonization and testing will now need to address the relevance of each measure.

More than a decade ago, a coalition of purchasers, payers and providers came together across Wisconsin to form the Wisconsin Collaborative for Healthcare Quality (www.wchq.org). Groundbreaking initiatives like Get with the Guidelines, Leapfrog and JCAHO  revealed that “quality” and “healthcare” could be used in the same sentence (or displayed on a website). These efforts were largely inpatient-focused. Measurement in the outpatient setting, long considered the keystone of payment reform, was an unsolved riddle. WCHQ, at the urging of the IOM, IHI and others accepted the challenge of tackling performance in the ambulatory arena.

At the direction of some very engaged employers, and with input from most of the state’s payers, WCHQ was charged with one very simple goal — apples to apples quality measurement, regardless of health IT infrastructure. The focus had to include both processes of care and outcomes. Oh, and if health systems didn’t have any health IT in place, data still needed to be included for these groups in the measurement effort. What transpired over an 18-month period was remarkable. With unwavering support from administrative and clinical leadership, health systems rolled up their sleeves and dug into their very messy data. Each Monday, we would devise a fiendish list of new tasks to be completed in the next four business days.

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How Fit Is Your Healthcare Data?

Michelle Blackmer

Guest post by Michelle Blackmer, director of marketing, healthcare, Informatica.

Several weeks into the New Year, our fitness resolutions are still top of mind. Whether tracking calories or steps, we are asking ourselves questions like “how many pounds have I lost?”, “how many calories did I eat?” and “how many steps did I take?” To take the guesswork out of it and to hold ourselves accountable, many of us put a Fitbit, Nike Fuel or Jawbone on our wish lists. Our physical fitness has become data-driven; these devices create data that provide insight, enable us to visualize patterns and generate millions of bytes of data, which helps account for the anticipated annual 40 percent growth in big data. However, this is only the tip of the iceberg for data-driven healthcare.

Health information leaders must continue to assess their business resolutions and take stock of their healthcare data fitness. This is especially important since an alarming 40 percent of healthcare executives gave their organizations a grade of “D” or “F” on their preparedness to manage the data deluge. What’s more is that none felt their organization deserved an “A.”

Successful transformation to value-driven care requires an investment in enterprise information management. However, healthcare organizations are tightening their belts and bracing for the hit to their bottom lines in response to the health reform law that took effect on January 1, 2014. Instead of scaling back, healthcare organizations must invest in the fitness of their data. After all, if the wrong data is analyzed (i.e., inaccurate, incomplete, missing or even unnecessary), organizations are going to make the wrong decisions. What is the cost of making the wrong decision?

Assess your data fitness. Ask yourself the following questions:

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