Category Archives: Big Data

Need a Data Scientist? Try Building a ‘DataScienceStein’

Category : Big Data

Organizations are finding that hiring qualified Data Scientist is a real challenge. Experienced Data Scientists are expensive and are usually employed elsewhere. This high demand, low supply economics is leading to a situation of the ‘haves’ versus the ‘have-nots’, where the larger, financially rich organizations in the ‘sexy’ industries are most capable of attracting and hiring data scientists, while the lesser companies will have to make do without one.

Organizations are looking at new approaches to finding data scientists. Some are able to attract them with more than money like autonomy and development opportunities. Others are training current staff to become more data literate through professional development programs. Once trained, these individuals typically must work 12 to 24 months at the organization or have to pay back the amount spent on their training.

There is another approach that should be considered.

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Predictive Analytics World Chicago 2016 Recap

I attended Predictive Analytics World in Chicago the week of June 20 to June 23. I met a lot of new people and was reacquainted with several other colleagues. As I listened to 2 days of workshops and the pre- and post-conference workshops, some common themes emerged. Most of these themes confirmed what I have been touching on in the presentations I’ve made at conferences over the last few years and discussed in my book, Competing On Healthcare Analytics, but it was reassuring to hear the same concepts presented by others.

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IBM Watson Health Closes Acquisition of Truven Health Analytics

This could have a significant impact on moving the industry closer to becoming data enabled healthcare.

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Top Challenges to Analytics in Healthcare? Not Technology!

A variety of challenges stand in the way of successfully implementing analytics in healthcare organizations. Not surprisingly, the top issues don’t always involve technology.

This finding became clear in a study conducted by the Healthcare Center of Excellence this summer, which sought to determine what are perceived to be the top challenges facing analytics.

The study reveals the importance of executive leadership skills in bringing about support of analytics and the extent to which findings from analytic efforts are incorporated into how organizations change and adapt. This aspect of leadership, while learnable, needs to happen quickly if organizations want to achieve the desired incomes from their forays into analytics.

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Medical Informatics World 2016

April 4-5, 2016, Boston, MA

Now in its fourth year, Medical Informatics World has become a must-attend industry event, uniting senior-level executives and industry leaders representing all the major contributors to a new era of healthcare. More than 400 providers, payers, technology providers, biomedical scientists, academic researchers, informaticists and national health organizations come together to discuss emerging trends and collaborations in health IT for improved outcomes in the healthcare ecosystem. Focused tracks allow the community to delve into the most pressing topics of cross-industry data sharing, population health, patient engagement, and clinical decision support. Keeping pace with the evolving industry, coverage has now expanded to include quantitative imaging and radiomics, predictive analytics and interoperability.


Using data to move toward transparency: Six things health care providers can do

With high-deductible health plans increasing in popularity, cost and accountability for outcomes will become more important to consumers seeking medical providers. Additionally, provider organizations managing risk-based contracts will have additional motive to increase transparency to earn trust and instill loyalty with patients.

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Cancer Centers, Epic to Tap Power of IBM Watson Supercomputer

A supercomputer like IBM’s Watson is needed to be able to analyze the large number of detailed records needed to “develop patient treatment protocols, personalize patient management for chronic conditions, and intelligently assist doctors and nurses by providing relevant evidence from the worldwide body of medical knowledge, putting new insight into the hands of clinical staff.” The key to the success would be able to share and analyze patient-specific data in real time, a part of the standard workflow. This will allow Watson to “bring forth critical evidence from medical literature and case studies that are most relevant to the patient’s care.”

Cancer Centers, Epic to Tap Power of IBM Watson Supercomputer

Epic, Watson at work on interoperability

IBM’s Watson targets cancer and enlists prominent providers in the fight

 


Cleveland Clinic makes analytics available

This is an interesting development. It may be the only way some smaller hospital systems receive any assistance with healthcare analytics.

Cleveland Clinic makes analytics available


Data analytics top concern, but industry stumped about where to start

This is not unexpected. I discussed this problem at the Healthcare Analytics Symposium in July 2014. Even if most healthcare organizations knew where to start, they would still be missing the talent and data management capabilities to be effective. #beginhealthcareanalytics

Data analytics top concern, but industry stumped about where to start


No interoperability? Goodbye big data

Interoperability is extremely important to creating large datasets for analysis. Even with interoperability, there’s a huge gap in how most healthcare organizations gather and connect their own data. Without an internal data strategy, interoperability with outside systems will have little impact. #nointeroperability

No interoperability? Goodbye big data