A slideshare sum up of a 43 page ebook addressing the data challenges that frequently come up in the Healthcare Industry and how to address them efficiently. Download the complete version at http://info.dataiku.com/ebook-healthcare-datadriven-scheduling/

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Advanced Analytics for Efficient Healthcare
Data-Driven Scheduling to Reduce No-Shows

2. One Dataset A Day
Won’t Keep the Doctor
Away
The healthcare industry has a data problem
The fact is that there is an abundanceof raw data and no one
really knows what to do with it. The goodnews is that allof this
data can beused to solvea multitude of common, day-to-day
problemsusing predictiveanalytics.

3. This ebookaims at providinghealthcareprofessionals
with a clear view of how efficiencygainscould be
realizedat littlecost via the integration ofdata
analytics.
We will start by having a lookat what is wrongwith
the current implementation ofdataanalyticsin the
healthcare ecosystem andhow it appliesto the no-
show issue.
We will then offer an alternativeapproach to
addressingno-show appointmentsthatmakes use of
predictiveanalytics.
Lastly, we will discuss how this method couldbe
appliedto the healthcare industry.

4. Data Fragmentation and Limited Skills
Deteriorate the Data Analysis Process

5. Figuringout what to do with allof the datais a
challengethat liesat the heart of medicinein the
21st century.
The “dataissue” is exacerbatedby the nature of
the U.S. healthcare ecosystem: it is a highly
fragmentedindustry across multiplesectors.
Healthcare is rarely coordinated, incentivesare
misaligned, andvariation isubiquitous.
Data Fragmentation and Limited Skills
• Multiplicityof Data Sources Makes Collection &Use Difficult
• Data DiversityHinders Data Integration
• LimitedHuman Skills InhibitEffective Data Analysis

6. Data Management Challenges Illustrated
by the No-Show Issue

7. The ubiquity ofno-shows has put a spotlighton a
set of broaderdata managementissues in the
healthcare industry.
The inabilityofhealthcare organizationsto deal
with the no-showissue has had a profoundeffect
on patienthealth, their experienceswith
healthcare providers, andon the financialbottom
line.
The problemis a difficult oneto solve due largely
to industry practices that are botharchaic and
ineffective.
Data Management Challenges: the No-Show Issue
• The FinancialandHuman Cost BehindNo-Shows
• The 3 Main Barriers to Solvingthe No-Show Issue
• 4 Quick Fixes…That Don’tWork

8. Step By Step Methodology to Build your
Scheduling Data Product

9. The process of developingadataanalytics
strategy to tackle the no-show problemrequires a
comprehensivemethodology.
The approach should start with defininga
tangiblegoalandendwith incorporatingthe
output into business practices;
Between those two end-pointswehave a
completeagile-basedprocess that includes data
definition, gathering,cleaning, processing, and
improving.
Step by Step Methodology to Build your Scheduling
Data Product
• Defining the Perfect Framework
• Order Out of Chaos: Collecting & Making Sense of Data
• A Predictive Model to Test your Hypothesis
• From Theory to Practice: Deploying your Data Product

10. Creating a Proper Data Structure for a
Complete Analytics Methodology

11. Data analyticsin the healthcare industry has a
bright future. This is due to a number of factors
workingtogether:
• huge amountof data (150+exabytes as of
2012);
• A desperateneedto understandraw data and
assign meaning;
• The significantnumberof business-oriented
applicationsto which data analysis can be
appliedin the healthcare sector.
In orderto successfully implementapredictive
analytics solution in the healthcare industry, itis
necessary to have a clearvision of outputs,
implementIT systems that are interoperable, and
have a commitmentto knowledgesharingacross
the organization.
Creating a Proper Data Structure for a Complete
Analytics Methodology
• To KnowBefore You Go
• DevelopingSystemInteroperability
• Fosteringthe Distribution ofSkills & Knowledge
• A Shift from Retrospectiveto ProspectiveAnalytics
• Engagingwith Patients: Nowor Never

12. Curing the Healthcare Industry One Data
Product at a Time
• The no-showissue has nowreached a stage where the
healthcare industry, as a whole, is losingbillionsofdollars each
year. Attempts to fix the problemarereally stopgapmeasures
designedto address the symptoms.
• Data analyticsenables organizationsto stopthe guesswork and
understand exactlywhen specific patientsare likely/unlikelyto
appear for anygiven timeslot.
• No-show is a singular problemin a vast sea of possibilities. The
realityis that the healthcare industry faces a plethora of
challenges whose solutionsrevolvearound vast amounts of
untappeddata.
• The possibilitiesforpredictiveanalytics are endlessand are
indicativeofthe world we livein: where vast quantities of raw
data can beaccessed, cleansed, collected, parsed, formatted,
andelegantlyvisualized in a meaningfulway.

13. Download the entire Ebook!
http://info.dataiku.com/ebook-healthcare-datadriven-scheduling/