Latest in Health IT

Six percent increase in Big data analytics adoption in Healthcare since 2013

According to a new issue brief, by HIMSS Analytics, the adoption of clinical analytics and business intelligence (C&BI) infrastructure is on the rise. An additional six percent of provider organizations have implemented some form of advanced clinical or financial decision-making technology. They harness large, varied sources of data since 2013, tipping more than half the industry into reliance on these important tools.
The HIMSS Analytics brief pronounces that providers have shifted their focus from value-based reimbursement and accountable care to population health management. They are taking this change as the core competency and most likely to benefit from focused infrastructure investment. More than half of registered hospitals in the U.S are using some population health management tool to scale risk, aid chronic disease care, and improve coordination across the care continuum.
The predicted compound annual growth rates of up to 44% for clinical analytics technologies that is driving billions of dollars in sales as healthcare organizations modernize their aging legacy systems and implement new functionalities.
These technologies already have a measurable result in patient care. The deployment of improved analytics infrastructure is likely to maintain its upward trajectory in near future.

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Five obstacles to population health management

Here are five big obstacles to population health management:
1. Lack of compliance by clinicians

Although many leading health networks have initiated evidence-based pathways, protocols and decisions tree outlining, how they want physicians and nurses to treat patients. Compliance with these rules by clinicians in the real world remains challenging.

2. Little clinical care coordination for patients

As numerous and various technologies coming to market, poor adherence stays, and it leads to massive overutilization of the system.

3. Cost & Time

The emerging practice, something of a precursor to the move from predictive to prescriptive analytics, requires providers taking a deep look at their data to pinpoint patient populations that consume large percentage and costs of care, then select the ways to bring and impact and change to those population subsets.

4. Excessive data

While it might seem the more information the better, the industry is being threatened by excessive data. The big possibility for improving care and lowering costs is to sum claims data with EHR and other systems and running analytics against them.

5. Less patient engagement

Providers need to learn better how to engage their patients because it is the key to excellent outcomes, reducing overutilization and ultimately slashing costs.

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Predictive analytics helps identifying high-risk Hepatitis C patients

According to a study published in recent issue of Hepatology, researchers have developed a predictive analytics algorithm that uses basic EHR data to flag patients at high risk of developing complications from the hepatitis C virus (HCV). As payers seek to control the costs of extremely expensive HCV treatments like Sovaldi, the use of clinical analytics may help to avoid needless spending.
More than three million Americans are living with HCV, the Office of the National Coordinator said recently. The virus presents a significant challenge when it comes to reducing the costs of chronic disease management. Approximately 1/3 HCV patients are at high risk of developing complications from the virus.The team applied machine learning techniques to treat clinical information such as lab results, age, body mass index, and details of the virus type to create a risk score for patients. The score is more reliable than earlier attempts because the algorithm uses more lab values than other models and analyzes how the values change over time.
The tool can be integrated into EHR to help providers deliver more aggressive treatment to patients who would see the most benefit. It can also help healthcare companies develop a chronic disease management plan by identifying ideal intervals for primary care visits and follow-up. This is especially important for HCV patients, who often fall away from the continuum of care.
By combining predictive analytics and care coordination aids that help providers ensure higher levels of chronic disease management and medication adherence for patients, the healthcare system may be able to reduce waste and offer more treatment options to those who have a higher chance of developing life-threatening complications.

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Seventy Six percent of Hospitals engaged in electronic sharing of information last year

More than three-quarters of non-federal acute care hospitals reported electronically sharing health info with outside providers in 2014 according to a report released by ONC.

The survey found that 69 percent of hospitals electronically exchanged health information with ambulatory providers outside their organization, a 92 percent increase since 2008 and a 21 percent increase over 2013.

According to the report, prior to the passing of the HITECH Act, only 15 percent of hospitals involved in electronic exchange of health information with providers outside of their systems, although exchange with outside ambulatory care providers was significantly higher. The competition among hospitals and cross-vendor interoperability have historically been cited as obstacles to free exchange out-of-network. The current study has found that gap is narrowing, however, with the percent of hospitals electronically exchanging clinical data with out-of-network hospitals at 62 percent – quadruple that of 2008, and a 55 percent increase from 2013.

In addition, the gap in variation by type of data exchanged has also begun to close, with 69 percent of hospitals exchanging laboratory results, 65 percent sharing radiology reports, and 64 percent sharing clinical case summaries with outside hospitals. And 58 percent reported electronically sharing medication history with outside providers.

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How Not to Cut Health Care Costs

“Health care provider organizations also try to optimize the number and mix of patients seen—for instance, by pushing physicians to spend less time with each patient and on treatment processes that are poorly reimbursed under fee-for-service mechanisms. Fee-for-service payments encourage physicians to increase their volume of reimbursable procedures and visits, not to deliver effective and efficient care for a patient’s condition. To make matters worse, clinical personnel—the people who actually treat patients—are seldom involved in decisions about how to achieve savings, which means that providers lose out on significant opportunities for benchmarking and standardizing medical practices in ways that could both lower costs and improve care. Field research we are conducting with more than 50 health care provider organizations, most U.S.-based, suggests much better ways to reduce costs without jeopardizing care and often while improving outcomes.”

Robert S. Kaplan and Derek A. Haas

Harvard Business Review

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Patient Engagement through Smartphones

Patient engagement approaches are essential for healthcare providers who are looking to reach the latest objectives of the meaningful use requirements. Although the Medicare and Medicaid EHR Incentive Programs focus mainly on patient portal adoption and usage, mobile health applications can play a necessary role in engaging patients in their overall wellness and care.
The utilization of mobile phone applications in the healthcare environment is at the initial stages of deployment. The mobile health tools can be useful in managing patients with chronic diseases.
Mobile health strategies can better engage patients who live in poorer communities and are harder to reach. The text messaging, and Internet-based applications can target the health of patients who have previously been underserved with healthcare services.
Due to its low cost of implementation, text messaging may be a viable option for generating greater patient engagement with preventive care services and wellness activities like diet and exercise.
When it came to some natural barriers to mobile health implementation. The lack of external funding, limited technical resources, and insufficient integration of mobile health tools with EHRs and health IT systems were all leading to lower rates of mHealth adoption.

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