Archive for Healthcare Data Mining

Eight Ways Physicians Know They’re Overworking

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Overwork is not pretty, and in some cultures it’s deadly. In Japan, “karoshi” or death from overwork, annually claims anywhere from 10,000 workers to 30,000 workers. The range is vast because, without autopsies, it’s difficult to accurately assess the cause of death of people at their desks, slumped over.

Karoshi does not appear to be a significant phenomenon in the U.S. Still, among over-workers and the highly fatigued, high blood pressure and heart disease are exceedingly common.

Danger ahead

Given that you work very long hours — why can that be dangerous? When you encounter stressful situations by working longer and harder, your muscles contract, your blood thickens, your heart pumps blood faster, and your arteries narrow. You’re prepared for fight or flight. If you actually did fight or flee, the situation would largely take care of itself.

Instead, your internal “engine” is revving for eight hours to 10 hours on end. You arrive home, where more stressors may emerge. You cannot sleep as many hours as your body requires, or if you do, it’s fitful sleep with tossing and turning. As a result, you’re being worn down and your immune system is becoming weaker. Thus you’re more susceptible to illness.

Some researchers believe that consistently having too little sleep could impact your whole life, to your detriment. Combined with too much work and too little sleep, any illness that you might contract can be more troublesome.

Beyond tired

You feel tired, but when are you bordering on danger? Among many signs, here are a few:

1. Lack of appetite or indigestion. You normally look forward to meals, but when highly fatigued, you have trouble getting them down. Maybe, you’re eating less. Your fatigue is prolonged.

2. Extra sleep doesn’t help. Getting many nights of extra sleep in a row or sleeping for an entire weekend doesn’t seem to diminish your fatigue. Perhaps worse, you feel as if you’ll never “catch up.”

3. Excessive sleepiness. You doze at inopportune moments, such as during an important meeting, or when driving!

4. Loss of sex drive. This isn’t obvious because decline in libido usually occurs a bit at a time and you don’t notice, although your partner likely will.

5. Interrupted sleep. At night, you wake more often or toss and turn, and then, worse, you spend the rest of the night overly concerned that you’re not attaining good sleep.

6. Persistent fatigue. You feel tired upon arising even after a full night’s sleep. Realistically, if, by 9:30 a.m. or 10:00 a.m., you can hardly keep your head up, it’s time to take heed.

7. Poor concentration. Your focus on the task at hand is poor. Your concentration is diminished and is not due to your aging.

8. Feeling ineffective. Finally, you feel that you’re no longer in control. In many ways, this can be the most worrisome of all signs. Roll back your number of working hours as soon as you reasonably can.

If one or more of these has been a lingering issue for you, it’s time to take a personal inventory and make some decisions about how you are going to change things

Source: http://www.physicianspractice.com/blog/eight-ways-physicians-know-theyre-overworking

The Power of Big Data

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Harnessing and capitalizing upon the monstrous amounts of available healthcare information

By Peter Edelstein, MD, Elsevier

Big Data. Population Health Management. Patient Engagement.

Healthcare reform churns out buzzwords at an alarming rate.  But at least big data has a more defined meaning, having come to linguistic life long before the Affordable Care Act was a gleam in President Obama’s eye presumably.

Today’s world runs on big data.”  It’s big data that allows millions of us to almost instantaneously receive insurance quotes online; creates your credit score; select a mortgage; and pushes pop-up advertising that just happens to be exactly what you were looking for yesterday.

As is our healthcare industry’s history, big data is yet another capability that has entered the medical arena long after becoming an integral part of non-medical sectors.  That said, big data is (finally) here to stay, in our hospitals, our pharmacies, in our insurance systems (where it has been the longest), and in our ambulatory care centers.

Big Data Goals

And like population health management, patient education, and other buzzwords, understanding our specific big data goals and how to achieve them is critical if we are to maximize the success of healthcare reform.  So the first question is, What Are Our Healthcare Goals for Big Data?

If an underlying goal of healthcare reform itself is to improve the quality and cost efficiency of care for populations and for individual patients, then we must turn away from reactive care provided in the acute, inpatient facility and strive for proactive, preventative, and maintenance care provided in the ambulatory world (both the outpatient physician office and in the place where patients spend virtually 100% of their time:  their homes and workplaces).

Linking to this goal, Big Data can drive the identification of individuals and populations at risk of suboptimal quality and/or cost of care and then to guide intervention to reduce or prevent the realization of the identified risks.

Already, Big Data is playing a foundational role in the first part of this goal.   Monstrous amounts of claims data serve to feed clinical analytics models, including predictive models.  Such powerful tools allow us to predict which patient populations and individuals are at risk of specific forms of clinical deterioration, high cost care, and/or unanticipated hospitalization and Emergency Department visits.

And recently, the incorporation of public records Big Data (including moving, home ownership, eviction, lien, and property value history; estimated annual income, wealth index and financial stress; and accident, fraud, burglary, and criminal history) along with health claims data has allowed for the development of even more powerful predictive analytics models.  (For example, inclusion of such non-medical data may more accurately predict risk of early post-discharge hospital readmission and/or risk of failure to pay).

Big Data Expansion

Today, non-clinical Big Data is expanding from the clinical analytics world into site of health care delivery.  The Institute of Medicine is recommending the inclusion of social and behavioral data within the EHR, where (as with analytics) this expansion of Big Data is projected to more clearly and accurately guide patient care.

Whether empowering clinical analytics models or more clearly defining individual patients and populations from within the EHR, the ultimate impact of our evolving Big Data is in guiding evidence-based content and clinical decision support tools which are targeted to meet the specific needs of an identified patient population, subpopulation, or individual, content which can be pushed to every point of care (hospital, ambulatory setting, patient home, etc.) and delivered in a format appropriate for the specific provider (doctor, nurse, patient, etc.).

As we widen the net of data sources and types included within our analytics models and electronic information systems, we are increasing our ability to hone down, to fine-tune our understanding of specific and populations’ and patients’ risks, needs, and opportunities to improve both the quality and cost efficiency of their care.  To provide the most appropriate evidence-based content wherever it needed, whenever it is needed, for whoever needs it.

Peter Edelstein is chief medical officer, Elsevier Clinical Solutions

The key to improving healthcare: It’s about the data

To conclude MSLGROUP Boston’s HIMSS15 video series, Senior Media and Content Strategist Don Fluckinger spoke with three clients about what they were showcasing at their booths, and what problems they solved en route to improving healthcare. While these vendors work across very diverse areas of healthcare IT spectrum, the one focus they have in common is working with data: Patient data, clinical reference data and data security.

EBSCO Health’s VP of Medical Product Management and Chief Content Officer Betsy Jones showcased the company’s new product DynaMed Plus™. Rebuilt from the ground up, this evidence-based clinical reference has a new taxonomy that speeds time to answer access to needed clinical data, a new interface to maximize efficiency on mobile devices at the point of care and much-expanded content. These new features add up to a clinical decision support tool that aids physicians in choosing the correct care paths for their patients.

Derek Gordon, general manager, health information and technology group at Healthline, spoke about the company’s Coding InSight application, which is built on its HealthData Engine, a data analytics platform that harnesses the power of structured and unstructured data. Coding InSight uses the HealthData Engine’s unique data mining capabilities to uncover missed or inaccurate codes from unstructured patient data, infers which correct codes should be applied, and closes those gaps prospectively and retrospectively in near-real time. This helps clinicians improve efficiency, reduce costs and accurately realize reimbursements, while optimizing risk scoring of patients and facilitating timely interventions for improved outcomes.

Stephen Cobb, senior security researcher at ESET, talked about the company’s strength as a global leader in proactive digital protection, offering anti-malware, encryption and authentication solutions. He advised healthcare organizations to be more suspicious about the kinds of abuse of their systems, both inside and outside the organization. He is seeing healthcare organizations trying to comply with standards, such as the HIPAA “omnibus” rule, which mandates new levels of security protection. But he warns organizations to have less of a “check-the-box” approach and be more proactive about creating a strong security infrastructure.

Do you have a product launch coming up? Wish to get into the healthcare data conversation? Contact Doug Russell (781-684-0770) in MSLGROUP’s Boston office to learn how we can help with your strategic communications programs

Mining data for state CDC, Maine HIE pilot project aims for population analysis

The Maine HealthInfoNet is aggregating and analyzing health information exchange data at the population level, with the aim of finding trends and specific figures that currently evade most tools of epidemiology.

In a pilot project funded by the Centers for Disease Control and Prevention, HealthInfoNet, Maine’s statewide HIE, is collecting and assembling data for the Maine CDC, using the open source software popHealth. The project focuses on 13 Meaningful Use clinical quality measures using the ABCDS — aspirin therapy, blood pressure, cholesterol and diabetes control and smoking cessation.

It should let public health researchers find out, for instance, what percentage of Mainers with diabetes have sugar levels under control and what percentage of hypertension patients had their blood pressure checked during their last medical visit.

[Related: CDC to use Direct protocol for health safety network.]

These and other public health measurements, at least of large populations, have eluded researchers for a while, said Stephen Sears, MD, Maine’s state epidemiologist.

“If you want to know how many diabetics within a database there are within a certain age group,” Sear said, “that’s almost impossible to get right now unless you have a data set like the Maine HealthInfoNet registry.” Plus, you need the technology to sift through it all while staying HIPAA compliant.

Sears is cautiously optimistic that they’ll be able to find all of what they’re looking for in the various clinical areas. “What I’ve seen is that it suggests that for certain parameters it looks like its going to be able to work.”

The data basically runs from HealthInfoNet and its vendor, Agilex, to popHealth and then to the Maine CDC. The U.S. CDC’s role is mostly funding and support, through its program Demonstrating the Preventative Care Value of Health Information Exchanges.

The project and a lot of data collection started a little less than a year ago. Now HealthInfoNet and the Maine CDC are essentially testing their capabilities.

[See also: A look at how Maine’s HealthInfoNet is turning grant money into actionable outcomes data.]

“For every person, each month we’re producing different measures for people based on conditions,” said HealthInfoNet CEO Devore Culver. They’re able to make comparisons like how many diabetics who’ve had a hemoglobin lab test scored under 9, an indication of diabetes control.

“The state CDC expends a significant amount of energy and effort trying to gather and compile data that allows them to draw conclusion about trends of health in Maine,” Culver said. “Up until now, it’s been a fairly manual-intense process and the data is not always clear.”

Culver noted that the project is first of its kind and could be a boon to the Maine health department and CDC.

“It’s really a first foray into whether you can repurpose information, not violate patients privacy or expose providers, and use it for something of value and see how health is progressing in the state,” Culver said. “This is a very low cost, with a lot of value.”

If Maine can prove the analysis works, the goal is to eventually take the model to other states, said Dr. Taha Kass-Hout, Director of CDC’s Division of Informatics Solutions and Operations.

[Feature: A new age of biosurveillance is upon us.]

“We’re building a way for state health departments to use the data that’s circulating around their state health information systems,” Kass-Hout said. “The whole goal here for us is to be able to create shared services and platform for local and state health department to use this data.”

First, though, HIE organizations need to be able to meet certain technology and policy criteria that lets them navigate potentially rough waters, he said. HIPAA compliance is a major challenge, as is maintaining providers’ privacy.

“Maine Health InfoNet has the right governance, the right policies and it’s independent, non-for-profit — free of much political or industry influence,” Kass-Hout said.

The pilot project runs until the end of the year. Kass-Hout wouldn’t say if the CDC will renew funding for another year.

View the original article here

CMS EHR incentive payments flirt with $7 billion

Medicare and Medicaid electronic health record payments are approaching $7 billion since its inception, with $6.9 billion paid out to 143,800 physicians and hospitals in total program estimates through the end of August.

Final figures will be available later this month as the Centers for Medicare and Medicaid captures more complete data.

In August, the agency paid about $500 million in incentives, with about $325 million going to Medicare providers and $175 million to Medicaid providers, “which will bring us knocking on the door of $7 billion in incentive payments issued as of the end of last month,” said Robert Anthony, a specialist in CMS’ Office of eHealth Standards and Services. 

He reported the latest EHR incentive program statistics at the Sept. 6 Health IT Policy Committee meeting

In July, the totals were $6.6 billion since the program’s start paid to 132,511 eligible providers.

As of July, nearly 1 out of every 5 Medicare eligible provider, or about 18 percent are meaningful users of EHRs., he said. Additionally, 1 out of every 4 Medicare and Medicaid eligible providers has made a financial commitment to an EHR, he said. And 55 percent of eligible hospitals have received an EHR incentive payment for meaningful use.

As of July, 271,105 Medicare and Medicaid physicians and hospitals have registered to participate in the incentive program, tracking at about 10,300 monthly, he said. Breaking down the total, that’s 180,513 Medicare physicians, 86,708 Medicaid clinicians and 3,884 hospitals.

[Survey analysis: Romneycare vs. Obamacare, do Americans care?]

Even as more physicians and hospitals participate in the incentive program, nothing has changed related to their level of performance in the attestation data, Anthony said.

“The longer we go saying that not much has changed, the more encouraging that trend actually is because it is an indication that more and more providers are coming in, yet everybody is performing at a statistically high level,” he said.

Providers tend to exceed the required threshold of performance for recording objectives for problem list, medications list or medication-allergy list. And there is little difference in performance among physicians and hospitals, Anthony said.

“As we move into August, we’re no longer looking at just the early adopters, we’re looking at people who may still be in their first year of meaningful use, but they’re not necessarily the people who are at the beginning of the curve. Yet we continue to see very high performance across the board on all the objectives,” he said.

“We will be better informed when we have people returning later in 2012, and we can do a comparison of meaningful use in a second full-year period vs. a 90-day period,” Anthony noted.

Besides the required measures, the most popular menu objectives to attest to are advance directives, drug formulary and clinical lab test results for hospitals; and drug formulary, immunization registry and patient lists for physicians. The least popular measure is the transitions of care summaries for both.

View the original article here