Clinical Intelligence – #HITsm Chat Topic

Posted on June 20, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 6/23 at Noon ET (9 AM PT). This week’s chat will be hosted by Megan Janas (@TextraHealth) on the topic of “Clinical Intelligence.”

The word “Intelligence” is on the move in organizations. Creeping out from a corner of business that was once reserved for planning, strategy and competitive analysis- the intelligence of today is found in departments and teams and increasingly in the software suites that assist people with work. In hospitals and healthcare, clinical intelligence has deeper meaning than just “what AI programs are on the horizon and which one might we use.” Clinical Intelligence is dynamic, requiring multiple decisions and multiple steps to drive the sweeping change needed to usher in a new era of work and patient care. Healthcare will face challenges with next generation tech. However, with the right teams, forward thinking, and change agents, professionals can acquire meaningful Clinical Intelligence to transform their organizations and the patients they serve.

Let’s look at what defines Clinical Intelligence in order to break it down. An article from HIMSS describes Clinical Intelligence as:

“Clinical & Business Intelligence (C&BI) is the use and analysis of data captured in the healthcare setting to directly inform decision-making. It has the power to positively impact patient care delivery, health outcomes and business operations.” –Source

Clearly, Clinical Intelligence is in every level of a healthcare organization. That’s important, because for Clinical Intelligence to impact all areas, it has to be intentionally networked into each department. Clinical Intelligence thrives with interoperability, data, and analytics converging to help organizations make informed decisions from patient care to financial assessments. Teams need to evaluate their current capabilities, plan, and employ leaders with strong communication skills to convey the vision and objectives. This begins with a snapshot of where an organization falls on the data analytics spectrum. Descriptive, Predictive and Prescriptive Analytics make up the spectrum. Descriptive analytics tell a team about what has already happened from data collected around clinical documentation, claims, surveys, and lab tests. Predictive analytics takes the Descriptive data to make conclusions about future events. Lastly, Prescriptive analytics goes beyond prediction to reveal what steps to take should a prediction materialize. Moving through the data spectrum is an objective healthcare organizations will need to tackle to achieve CI.

In order to apply analysis to data sets, teams need to make sure the data that they have is relevant and large in scope to help guide their decision making. Additionally, professionals need to ask questions about data sets including, the type of data needed, the sample size, the available data, the bias that could be baked in, and if there are other sources of comparable data. The availability of public data is widely growing with resources including the US Department of Health and Human Services and the Centers for Medicare and Medicaid Services. Furthermore, the world of machine learning is assisting like never before, offering help by allowing teams to skip over data prep to pre-packaged data sets collected from a variety of sources. IBM Watson and IPsoft Amelia are just two examples of artificial intelligence machine learning making huge advances in several industries.

The data hospitals and others amass through their collective workings, build upon strategies organizations can deploy to reduce costs, improve care, assist with safety and patient outcomes. Suddenly, using data becomes an advantage, a competitive resource edging a health entity over their peers. The pursuit of Clinical Intelligence results in cross departmental learning and knowledge not previously available. Examples of Clinical Intelligence are found in a variety of healthcare settings. Wake Forest Baptist Health in North Carolina used analytics to assist in their oncology infusion center to assist with patient flow. The results were felt across the center with nurses less rushed and the pharmacy processing requests faster. Patients had fewer delays and overall the work environment improved. Montefiore Health System uses a predictive analytics tool to help identify patients at high risk of death or intubation within 48 hours of admittance. Mayo Clinic has additional tools to catch sepsis and treat it faster. These examples are just some of the ways in which analytics become valuable transformational assets.

The time to begin moving towards organizational Clinical Intelligence is presently with the preparation of data collection. Machine learning, and analytics offer health systems a new frontier of discovery; benefitting the decision making of every person involved in patient care.

Resources and Other Clinical Intelligence Reading:

  1. Clinical and Business Intelligence
  2. Turning Healthcare Big Data into Actionable Clinical Intelligence
  3. Four Keys to Successful Digital Transformations in Healthcare
  4. Better Questions to Ask Your Data Scientists
  5. The Most Valuable Resource is No Longer Oil, but Data
  6. Does Your Company Know What to Do with All its Data?

Please join us for this week’s #HITsm chat focused on Clinical Intelligence. We’ll use the following 6 questions as the framework for the discussion:

This Week’s Topics
T1: What are some benefits and obstacles to Clinical Intelligence? #HITsm

T2: How can health organizations best prepare for machine learning & AI? #HITsm

T3: Data has been described as “digital oil”. What’s its value and worth to a healthcare org? #HITsm

T4: How can leaders convince skeptics that Clinical Intelligence is valuable to an organization & patients? #HITsm

T5: How long do you estimate it will take for Clinical Intelligence to be within a healthcare system? Why? #HITsm

Bonus: Do you have an example of healthcare using analytics to learn? #HITsm

Upcoming #HITsm Chat Schedule
6/30 – EHR Optimization
Hosted by Justin Campbell (@tjustincampbell) and Julie Champagne (@JulieEChampagne)

7/7 – International EHR Adoption: Challenges and Solutions
Hosted by Stefan Buttigieg, MD (@stefanbuttigieg)

7/14 – TBD
Hosted by TBD

7/21 – Meeting the Patient Where They Are
Hosted by Melody Smith Jones (@MelSmithJones)

7/28 – TBD
Hosted by TBD

8/4 – TBD
Hosted by Alan Portela (@AlanWPortela)

We look forward to learning from the #HITsm community! As always let us know if you’d like to host a future #HITsm chat or if you know someone you think we should invite to host.

If you’re searching for the latest #HITsm chat, you can always find the latest #HITsm chat and schedule of chats here.