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


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

  1. 1. Notre Template Pour toutes vos présentations… Advanced Analytics for Efficient Healthcare Data-Driven Scheduling to Reduce No-Shows
  2. 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. 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. 4. Data Fragmentation and Limited Skills Deteriorate the Data Analysis Process
  5. 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. 6. Data Management Challenges Illustrated by the No-Show Issue
  7. 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. 8. Step By Step Methodology to Build your Scheduling Data Product
  9. 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. 10. Creating a Proper Data Structure for a Complete Analytics Methodology
  11. 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. 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. 13. Download the entire Ebook!