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Time to End ‘Wild West’ of Health Data Usage in HIPAA-Free Zones 

Time to End ‘Wild West’ of Health Data Usage in HIPAA-Free Zones  | Healthcare and Technology news | Scoop.it

In a recent conversation, a CMIO described the era of Meaningful Use and ICD-10 to me as the “doldrums of regulatory reform” that “sucked up all the oxygen” in the industry, leaving little room for innovation. So I can see why there would be little appetite for more regulation related to health data, and obviously the current administration prefers market-based solutions to regulatory ones.

 

Yet the Oct. 22 meeting, “Data Min(d)ing: Privacy and Our Digital Identities,” put on by the U.S. Department of Health & Human Services, made it clear to me that as more health data is gathered (and sold) outside the clinical setting, there is a “Wild West” atmosphere in which pretty much anything goes in terms of what companies not covered by HIPAA can do with our health data.

 

As an example, an April 2018 CNBC article noted that Facebook “has asked several major U.S. hospitals to share anonymized data about their patients, such as illnesses and prescription information, for a proposed research project. Facebook was intending to match it up with user data it had collected in order to help the hospitals figure out which patients might need special care or treatment.” (That project is currently on hiatus, Facebook said.)

 

The HHS meeting brought together industry leaders and researchers for some thought-provoking presentations about the many ways genetic, wearable and EHR health data is being used. For instance, James Hazel, Ph.D, J.D., a research fellow at the Center for Biomedical Ethics and Society at the Vanderbilt University Medical Center, presented his research that involved a survey of the privacy policies proffered by U.S. direct-to-consumer genetic testing companies. Hazel noted that there has been huge growth in direct-to-consumer genetic testing, with an estimated 12 million people tested in the United States. Beyond offering consumers the services, these companies doing the testing wish to monetize that data through partnerships with pharmaceutical companies and academic researchers. There is also value to government and law enforcement officials – to solve cold cases, for instance.

 

There is a patchwork of federal and state laws governing disclosure of secondary data usage to consumers, but the industry is largely left to self-regulate, he said. In his survey of 90 companies offering these genetic data services, “10 percent had no policies whatsoever,” he said. About 55 companies had genetic data policies, but there was tremendous variability in policies about collection and use. Less than half had information on the fate of the sample. In terms of secondary use, the majority of policies refer to internal uses of genetic data. However, very few addressed ownership or commercialization. And although almost all made claims to being good stewards of the data, 95 percent did not provide for notification in case of a data breach. The provisions for sharing de-identified data are even less restrictive. Hazel noted that 75 percent share it without additional consent from the consumer.

 

Hazel’s take-home message: “We saw variability across the industry. Also, we had a group of law students and law professors read the policies and there was widespread disagreement about what they meant,” he said. “Also, nearly every company reserves the right to change the policy at any time, and hardly any company provided for individual notice in event of a change.” He finished his presentation with a question. “What is the path forward? Additional oversight by the Federal Trade Commission? Or allowing industry efforts to take the lead before stepping in?”

 

In a separate presentation, Efthimios Parasidis, J.D., a professor of Law and Public Health at the Ohio State University, spoke about the need for an ethical framework for health data.

 

Parasidis began by noting that beyond data security and privacy, consent and notice are inadequate ethical markers. “If one looks at regulations, whether it is HIPAA, the European Union’s GDPR, or California’s recently enacted consumer privacy law, the regulatory trend has been to emphasize consent, deletion rights and data use notifications,” he said. While these are important regulatory levers, missing is a forum for assessing what is fair use of data.

 

“Interestingly, few areas of data collection require ethics review,” he stressed. HIPAA does not speak to when data use is ethical but rather establishes guidelines for maintaining and sharing certain identifiable health information. Even those protections are limited. HIPAA only applies to covered entities, he noted. It does not apply to identifiable health information held by a wide variety of stakeholders, including social media, health and wellness apps, wearables, life insurers, workers’ compensation insurers, retail stores, credit card companies, Internet searches, and dating companies.

 

“While the volume of identifiable health information held in HIPAA-free zones engulfs that which is protected by HIPAA and may support more accurate predictions about health than a person’s identifiable medical records,” Parasidis said, “the limits of HIPAA’s protections go beyond scope. For data on either side of the HIPAA divide, an evaluation of ethical implications is only required for human subject research that falls under the Common Rule. Much of data analytics falls outside the Common Rule or any external oversight.”

 

Citing the Facebook example mentioned above, Parasidis noted that tech giant Amazon, Apple, Google, Microsoft and Uber are entering the digital health space. “The large swathes of identifiable information that these entities hold raise a host of ethical questions,” he added, “including widespread re-identification of de-identified health information, health profiling of individuals or groups and discrimination based on health conditions.”

 

Policies and guidelines can supplement the small subset of data covered under legally mandated ethics review, he explained. For instance, federal agencies sometimes use internal disclosure review boards to examine ethical implications of data disclosure. But it is not clear this type of review is happening in the private sector.

 

Parasidis described work he has done with Elizabeth Pike, director of Privacy Policy in the Office of the Chief Information Officer at HHS, and Deven McGraw, who served as deputy director of health information privacy at HHS, on a framework for ethical review of how health data is used.

 

One way to think about more robust ethics review is the use of data ethics review boards, he said. Their structure can be modeled on institutional review boards or disclosure review boards. “This new administrative entity is necessary because much of contemporary data analytics falls outside existing frameworks,” he said. “We argue that these boards should focus on choice, responsiveness, accountability, fairness and transparency — a CRAFT framework. For instance, choice goes beyond consent. Individuals have an ongoing interest in their health data and should be able to specify how it is collected, analyzed and used.”

 

Reasonable minds can disagree on the relative weight of ethical principles or how they should be enacted into the context of data use deliberations, he said. “We nevertheless believe there remains an urgent need to craft an ethical framework for health data.”

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What Your Healthcare Organization Needs to Know About Measure Selection and EHR Configuration 

What Your Healthcare Organization Needs to Know About Measure Selection and EHR Configuration  | Healthcare and Technology news | Scoop.it

Participation in pay-for-performance plans like MACRA’s Quality Payment Program (QPP) poses new challenges for resource-strapped healthcare organizations. Many provider sites lack the time and technical expertise needed to retool their EHR implementation to document new metrics under value-based reimbursement models like the Merit-based Incentive Payment System (MIPS).

 

Nonetheless, that is precisely what clinicians must do to deliver on quality reporting requirements. When using EHR documentation tools, many factors must be considered for a provider to get credit on having met clinical quality measures (CQMs). How that information gets stored in the EHR directly affects reporting. Many healthcare organizations are finding that customizing an EHR to recognize when a measure has been met—preferably in a manner that poses as few interruptions to patient engagement as possible—is easier said than done.

Overcoming EHR Limitations

Many outpatient and inpatient settings still struggle with common EHR data management headaches. As clinicians bring new quality measures into the EHR, those underlying data management issues can foil even the best-laid reporting plans.

Discrete Data Capture

The push to better document clinical quality is causing a transition in EHRs to focus more on structured or discrete data that is easier to trend over time. Unfortunately, many healthcare providers still receive patient data from healthcare affiliates via fax. Those faxed documents show up as attachments in the patient chart and are not fully integrated into the patient data file. If that information was sent via HL7 interface instead, details on the care rendered by that hospital or other healthcare entity would flow into the EHR as discreet data variables. For many providers today, capturing that information in a manner that makes it usable in reporting and analytics still requires timely, manual data entry.

Documentation and Data Consistency

Provider sites with multiple clinicians may also encounter issues related to the slightly different way that each EHR user documents care. MIPS and other quality programs require consistency and a high degree of specificity in clinical documentation. If a clinician does not get diagnosis specifics into the patient chart, that patient may not be included in the CQM calculation they need to be included in. Many clinicians are having to modify their documentation process during patient encounters so they and the staff can capture all the necessary information in the EHR.

Clinical documentation will have even bigger repercussions under the Cost component of MIPS, which is slated to be factored into performance scores in coming years. Take, for example, a patient that is in for the flu. That patient has a certain anticipated cost impact (the average Medicare spending per beneficiary), calculated based on past medical history and services rendered. If a patient goes to a physician and has the flu but also has diabetes, heart failure, and asthma, that flu patient is probably going to cost more to care for. If the physician only submits the flu diagnosis and fails to document patient co-morbidities then the healthcare organization will not get the same allowance under the MIPS Cost category and could be labeled as “higher cost” than a comparable provider encounter for a patient that required fewer resources to care for.

Clinicians, coders, and staff need to make a mental transition away from “we’re submitting claims” to “we’re submitting data” to better serve clinical reporting initiatives and patient care analysis.

Making Informed CQM Selections

Beyond adapting to new data management processes, clinicians reporting under value-based programs also have a great deal to learn as they layer in additional quality measures under MACRA. One of the biggest challenges clinicians and administrators face is selecting the best measures for their specific healthcare organization. With limited spare time on their hands, many healthcare teams are leaning on outside expertise to help them evaluate the implications of various measure selections.

Measures Without Benchmarks

Many quality measures under MACRA are carry-overs or “relics” from other reporting programs. For these CQMs, providers can look to prior performance averages to evaluate the likelihood of success should the healthcare organization elect to report on those measures. That data does not exist for some CQMs, which are referred to as “measures without benchmarks.” On measures that have no benchmark data available, providers will be limited to a maximum of three reporting points instead of the ten points available on measures with benchmarks established.

To further complicate things, details on the availability of some benchmark data will not be calculated until after the March 2018 QPP reporting deadline. Providers may wish to further diversify or report on additional measures that could help offset low point earnings on measures without benchmarks.

Topped Out Measures

Another CQM caveat that providers should be aware of relates to “topped out” measures. These relic measures from other reporting programs are very engrained in many healthcare settings. Medication reconciliation, for example, was a requirement under Meaningful Use. Widespread adoption and universally high compliance rates on that measure makes it more difficult for clinicians to out-perform peers. Achieving maximum points on such measures requires a perfect or near-perfect score.

Keep average performance thresholds in mind when evaluating CQM selections, not just the healthcare entity’s individual performance track record. Look at a broader set of measures to maximize MIPS score potential. Clinicians could earn more points by scoring 70 percent on a non-topped out measure than they would earn scoring 95 percent on a topped out measure. Some topped out measures will likely be eliminated in future years to help diversify CQMs, as was the case under Meaningful Use.

Understanding the intricacies of CQM selection and EHR data management will be vital to success under value-based payment programs. Healthcare administrators and clinicians who proactively work to better understand the impact of various measures and streamline EHR processes will be best positioned to maximize program incentives.

 

Does your organization have the resources it needs to successfully navigate MIPS

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Digital Health Technologies for Alzheimer’s Disease

Digital Health Technologies for Alzheimer’s Disease | Healthcare and Technology news | Scoop.it

According to The Alzheimer’s Association, there are over 5 million Americans with Ad. It is the sixth leading cause of death. More than 15 million caregivers provided an estimated 18.1 billion hours of unpaid care at a value of approximately $221.3B. The impact of this disease is also well-illustrated in a recent  PBS documentary.  While it might seem incongruous on the surface to discuss digital technology and a population with significant cognitive challenges, I will illustrate how it can be beneficial at different stages of the disease’s course.

 

Cognitive Assessment Tools.  Most tools for assessing cognitive abilities have been of the traditional written form, as offered by the Alzheimer’s Association.  The ability of digital tools to detect early diagnosis of Ad is important in medical and social planning for the patient and family. Some have taken traditional diagnostic tools and transformed them into a digital platform. Such is the case with Quest Diagnostics’ CogniSense.  A more transformational approach is one seen with a utilization of the Anoto Pen which can measure the writing instrument’s position up to 80 times per second. An exciting study by the Lahey Medical Center and MIT’s Computational Science and Artificial Intelligence Laboratory looked at using the Anoto Pen versus traditional cognitive assessment tools for Ad and other diseases. This method has already shown advantages over traditional tools, described in an MIT News piece: “… while healthy adults spend more time on the dCDT [digital clock drawing test via Anoto] thinking (with the pen off the paper) than “inking,” memory-impaired subjects spend even more time than that thinking rather than inking. Parkinson’s subjects, meanwhile, took longer to draw clocks that tended to be smaller, suggesting that they are working harder, but producing less — an insight not detectable with previous analysis systems…”  A digital platform called Neurotrack claims it has the ability to detect Ad at its earliest stages by assessing recognition memory, a function specific to the brain’s hippocampal region which is affected early in the course of Ad. Digital assessment tools like these can also save clinician time and offer a better objective patient assessment.

 

Cognitive Improvement tools. A handful of small studies have shown that ‘brain exercise’ in the form of cognitive augmentation games decreases the risk in normal individuals of getting Ad. One would naturally ask if this carries over to those already diagnosed AD. Some earlier studies suggested this was the case. An older review of multiple small studies showed that while they suggest that brain exercises slowed progression of cognitive decay they did not affect mood or the ability to care for oneself.  It is worthy of noting that patients with larger baseline ‘cognitive reserve’ do better to a point then characteristically have a rapidly progressive course. In a previous post, I discussed the merits of music as an ideal digital health tool. Music should be considered as a potentially much appreciated and useful tool.  Relative to Ad specifically, I would reference the incredibly informative and moving award-winning film Alive Inside, documenting the response of patients with severe Ad to music relevant to their personal past. An intriguing interactive game/tool is Tovertafel, a Dutch technology which projects via suspended box visuals onto a table.  There are various exercises and games on the platform which are both enjoyable and mentally stimulating. Less sophisticated yet popular games are offered by the Alzheimer’s Association.

 

Tools for monitoring daily activities. Technologies have been developed to aid patients with mild to moderate disease and their caregivers to make daily activities easier and safer. SmartSole makes an innersole with a GPS locator with an associated smartphone app and call service for alerts. Silver Mother by Sen.se is a customizable digital tech platform (front door position, room temperature, and water and food containers) connecting caregivers with love ones’ activities of daily living.  For patients with early dementia or for caretakers to connect with loved ones at a distance, grandCAREis a very comprehensive platform and service.

 

While one might associate digital tools with those of us who are “connected,” their utility in the realm of Ad can be profound.  I would submit that the potential for digital tech to prolong independence and/or improve lives of caregivers in the home or at a distance must be the subject of clinical studies.  Public health policy might very well change as a result of such outcome studies.

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