Oncology Big Data: A Mirage or Oasis of Clinical Value?

PowerPoint Presentation

Oncology Big Data: A Mirage or Oasis of Clinical Value?

Michael Peters, MBA, CMC, CSSBB, R.T.(R)(T)April 15th, 2016

The title of the presentation, Oncology Big Data: A Mirage or Oasis of Clinical Value, reflects what I believe the field of Oncology is challenged with on a growing basis, from a clinical and business side perspective.

5/13/16US Cancer Specialist1

AgendaUS Cancer Specialist2A Clear Definition of Big DataThe Goal of Big Data in HealthcareBig Data Expansion in HealthcareProcess and Challenges of Capturing Big DataThe Future Value of Healthcare Big Data

Taking time into consideration, the Agenda here will cover the following as concise as possible. You may contact me further for more details or insights into todays presentation. A Clear Definition of Big DataThe Goal of Big Data in HealthcareBig Data Expansion in HealthcareProcess and Challenges of Capturing Big DataThe Future Value of Healthcare Big Data5/13/16US Cancer Specialist2

About the PresenterUS Cancer Specialists, LLC is a boutique Global Oncology and Diagnostic Imaging Consulting firm delivering Global Healthcare Insight to promote a Sustainable Delivery Model of Care, providing exemplary Patient Centric Care, while driving Profit and Market Growth Opportunity

Michael Peters, Founder and Principal Consultant

24 total years of Oncology and Imaging ExperienceHealthcare Data Analytic FuturistFormer Oncology Service Line AdministratorFormer Diagnostic Imaging DirectorMBA, Focus International BusinessCertified Management Consultant, CMCBS, Healthcare AdministrationAS, Radiologic Technology/Radiation Therapy


Big DataUS Cancer Specialist4

There wont be enough Data Scientists or Data Analysts. The reporting backlog is only going to grow for companies that rely on elite teams of BI analysts. McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts


US Cancer Specialist5

I include this slide in every one of my Big Data presentations simply because I feel it hits the point. In the medical field we are so use to the term, opinion. But at some point an opinion is not good enough, it is no more than a mirage. Individuals, or patients more precisely want answers based upon facts, they want the data to support the recommendation of care, to reach that oasis if you will.



"Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization**Gartner; Doug LaneyDefining Big Data

To clearly understand Big Data, we must first Define the term in its most general context. The definition you see here: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization was generated in 2001, buy an analyst, Doug Laney, from Gartner Analytics. It has become known as the famous three Vs, but since has added a fourth, Veracity to define that Mirage affect. In Oncology we have substantial amount of Data Volume, all at Rest or Historical, waiting to be Analyzed and Explored in greater detail. Velocity is the Real Time Data that we generate, for example, capturing treatment record data, capturing respiratory gating motion, etc. Variety is an area, we in Oncology are still young at. This type of Data continues to grow for Oncology, with the advent of increased Survivorship and Wellness and the unstructured data collection devices often used to collect this data, which we will explore later. Veracity was added to the 3 Vs for one key reason, to come to reason it truly isnt the quantity of data, but the accuracy of data that matters. Of the 4 Vs here, I believe Veracity is the key to providing the true value of Big Data. 5/13/16US Cancer Specialist6

US Cancer Specialist7Evolution of Healthcare AnalyticsSanders, D, Protti, D, Electronic Healthcare, 11(2), 2012 e5-e6

The triangle here is meant to represent the evolution of healthcare analytics and how far we have come toward patient driven quality and care. It is only because of the right analytical tools in place this has been possible. As we move from bottom to the top of the triangle, we recognize the following that has taken place over a period of time.

Insufficient, inconsistent versions of the truth, cumbersome internal and external reporting Collecting and integrating the core data contentRelating and organizing the core data contentEfficient, consistent production of reports and widespread availability in the organizationEfficient, consistent production of reports and adaptability to changing requirements. Reducing variability in care processes, Focusing on internal optimization and waste reduction. Tailoring patient care based upon population metrics, Fee for QualityOrganizational processes for intervention are supported with predictive risk models. Fee for quality Tailoring patient care based on population outcomes and genetic data. 5/13/16US Cancer Specialist7

InformationOptimizationForesightInsightHindsightDescriptive AnalyticsDiagnostic AnalyticsPredictive AnalyticsPrescriptive AnalyticsDifficultyValueThe Vision of Big DataWhat Happened ?Why did it Happen ?What will Happen?How to Make it Happen !US Cancer Specialist8

The collection of all this big data has to have an end game in sight, and for oncology it is setting forth that vision that will change the course of clinical care. But to get to that oasis, that stairway of value has to be ascended. For those oncology institutions still forging the way toward advanced analytics, the What and Why, also known here as Descriptive and Diagnostic Analytics is your day to day friend. Its the static reports that provide your daily, monthly and annual statistical clinical and business information in a retrospective manner. Useful data of the past, but not necessarily a certain prognosticator of the future. For those that have embarked on the advanced analytic platform journey, the What and How perhaps now have become the daily talk amongst not only your Financial Analyst friends, but also physicians alike. The ability to harness the 3 Vs with Certain Veracity through the use of advanced algorithms are setting forth the future of our field, just as it has for other industries that have been harvesting Big Data for the past 20+ years. 5/13/16US Cancer Specialist8

Historical Success of Big DataUS Cancer Specialist9

To have a discussion around Big Data, we cant neglect to cover the industries which truly have forged the way for data discovery and advanced analytics into the healthcare segment. The finance and business sector has profited from Big Data for over 20 years. Capital One for example was the first to use statistical modeling based upon public credit scores and demographic data to offer targeted credit offers. In doing such, their year over year net revenue has increased by 17% compared to an average of their competitors between 1%-11%. ITA by Google uses advanced analytics for predictive airline pricing. I think you may have heard of a few of their clients: Travelocity, Orbitz, every major airline. They are actually predicting based upon all the available combinations of connections, over 3 million on average, what the pricing will be for those flights. This is being used both by the airlines and by the consumers. CVS, a well-known major pharmaceutical and beauty retailer has effectively managed to use an ensemble of quantitative analysis and segmentation techniques to define and deliver outstanding customer experience, in contexts ranging from real-time execution to medium- and long-term planning, all based upon customer purchasing and the use of a loyalty card. So we can see, that healthcare in general is still what I would say in its childhood when it comes to big data, growing, but not having met its full potential, in part limited to its ability to what we will cover next, the Capturing, Processing, and Analyzing of the Data. 5/13/16US Cancer Specialist9

Capturing Big DataUS Cancer Specialist10


US Cancer Specialist11Structured Data:EHRSpreadsheetsUn-Structured Data: Mobile HealthText MessagingEmailSocial MediaLive ChatPatient PortalTele-health--------------------------------------The Tip of Data Discovery

When looking specifically at Data Discovery, we can reference back to the third V, Variety. We have become accustomed to the reports based upon the Structured Data which is most commonly found within our EHR systems. Structured Data has the advantage of being easily entered, stored, queried and analyzed. As a reference point, 400-600 tables out of 1000s are relevant to the current practice of medicine and its need for analytic use cases. But what hasnt been analyzed at this point in time is perhaps one of the most common forms of data found within an EMR: Unstructured Data. At its simplest form this constitutes scanned documents into a EMR system, which primarily are outside medical records including physician notes and prior patient History and Physicals. This here holds the majority of all clinically valuable data. Any key information, that is not transferred in a Structured manner cannot be queried, and therefore results in lost data, hindering effective predictive or prescriptive analytics. Additionally, Unstructured Data in the form of Mobile Health, Email, Social Media, Tele-health has shown potential in uncovering every day patient activity and health information. The best way for an organization to begin defining these data sources is to take an inventory of current and future potential data sources to identify possible options.5/13/16US Cancer Specialist11

Data CaptureExactly how people, processes and devices produce data determines:Appropriateness of data (capture the right stuff)Discreteness of data (capture in the right format)How easy it is to extract the data (capture in an accessible way)

PeopleProcessesTechnologyUS Cancer Specialist12

One of the top questions, I receive as a consultant is what data should be included in our EMR system? The should always revert back to what is the overall clinical and business goals within ones department. Capturing data just to capture data, has questionable benefit if there is no intent to analyze and change a course of path as a result of the provided data analysis. What needs to be focuses on is the process of Data Capture more precisely. As you see here, there are three fundamental that play a role here, People/Process/Technology. Its up to the Business/Clinical Staff to enter or capture the data correctly, the Veracity part of the equation. The process in which the data is captured is key to the usefulness of the Data, Structured or Un-Structured. And of course the Technology, the EMR system, or business/clinical information system to record the data, but then also the technology to process the data in an efficient and logical manner to provide a valued output. Without all three of these processes implemented correctly, the end result is analytical data of questionable value, providing limited or no actionable worth. 5/13/16US Cancer Specialist12

US Cancer Specialist13Data TypeSingle PatientCohort of 1M PatientsClinical Reports (PDF/Text)10MB10TBLab Results0.3MB0.3TBStructured EHR Data2.0MB2TBExome Genome Data125MB125TBImaging Data421.9MB421.9TBTotal559.2MB559.2TBRaw Genome Data5.7GB5.7PBGrand Total6.3GB6.3PB

Vojtech Huser, MD, PhD,* and James J. Cimino, MD (2015). Impending Challenges for the Use of Big Data. Int J Radiat Oncol Bio Phys 83. Retrieved from http://www.redjournal.org/article/S0360-3016(15)26657-6/pdf Storing the DataData is BIG!Storage is no longer a problem. Challenge is analyzing Data, both technically and humanly.

I believe this next slide to show favorable value, coinciding with the previous slide, to highlight the approximate size of Data being captured by patient. An average patient amassing 559.2MB of data is not too challenging to manage. A patient including a full genomic profile into their record, accounting for another 5.7GB can add up quite quickly for a health system. Imagine within Rad Onc, if all treatment planning data was captured within an EMR, not just the PDF summary, how that would contribute to the overall size of patient data. It is a good thing though that Big Data is no longer hampered by Data Storage. Data Warehouses and Data Cubes exist to help with not only storage, but also faster processing of the data. The challenge continues to be implementing technical analytical platforms that will cross reference the multitude of data reporting structures and their respective data warehouses to provide valuable data for a generally underserved analytical staff to analyze and produce action plans. This to me is an area where some of the most work has been put toward development as of late and is showing great promise, especially toward predictive and prescriptive analytics.


Data InterpretationDataChoose appropriate Business Question or Problem

Gather, understand, and prepare the data

Load prepared data into analytics tool or use in database algorithms

Deploy predictive model, integrate into reporting or application logicUS Cancer Specialist14

The Big Data Interpretation model here is intended to show the key steps from Planning to Deployment, based upon a strong business understanding of the relevance of the data being collected all the way through the deployment of actionable results of the data analytical findings. At any point during this model where data presents itself as underrepresented, new sources or a combination of two or more sources of data that will yield more insight should be evaluated for collection. All new and established data sources must be carefully examined to assess data quality levels and determine the appropriate type of corrective action. Assessments should compare data sources to established data quality targets and track improvements. Once data quality issues have been addressed, If at the Evaluation point that Data is questionable along with the Analytical Insight, then the model shall restart the process at the Data Understanding model to make sure it coincides with the overall Business Understanding. 5/13/16US Cancer Specialist14

The Goal of Big Data in Healthcare


Improving HealthcareImprove Quality and Safety

Optimize Resource Utilization

Evidence-based Outcomes

Right Value for Care

Increase Patient Satisfaction

Landscape changes+Gaps+New solution validationCost Effective, High QualityPatient-Centric CareUS Cancer Specialist16

Collection and Analyzing of Data is only as good as the intended purpose. In Healthcare, achieving Cost Effective, High Quality Patient Centric Care should be the primary focus behind all initiatives. As such, there is extreme pressure within the US to control costs of care delivery from both the patient and the payer side. The provider as well has to deal with the cost of care delivery, an area which still lacks the ability to provide an actual cost of care delivery per patient, verse standardized reimbursement. But the provider must also be prepared to address quality outcomes and patient satisfaction to coincide with the actual cost of care delivery. Advanced analytical platforms are available today to help address with these challenges

16


Big Data can enable more than $300-450 billion in savings per year in U.S. healthcare alone.*Predicting Cost of Care*Health Information Science and Systems 2014 2:3

Expanding on the prior slide, and the cost of care, Big Data has the potential to save 300-450Billion annually within the US. Moving beyond Fee for Service to Value Based Reimbursement will FORCE Operational Efficiency and Standardization of Care.By forcing the hand of the provider to change the process of care delivery, a better understanding of cost of care delivery will be seen through Operational efficiency, treatment care pathway, and regional cost of living; signaling the end Reimbursement based cost of care. 5/13/16US Cancer Specialist17

Expanding Clinical Research

3% Participation97% OpportunityUS Cancer Specialist1816,344 Active Cancer Clinical Trials

Cancer Patients Worldwide: 33M*US Initiates 38% of Clinical Trials*living with cancer, 2012 WHO

Beyond Cost is the Quality portion of Care. Right now, we havent even begun to fully explore where big data can help with advancing oncology care outcomes. The reason perhaps is that Clinical Research Trials have struggled to obtain participants which may have a wealth of clinical data to enhance clinical pathways. In the US there is only a 3% participation rate in Cancer Clinical Trials. This means that we are truly missing the opportunity to unlock knowledge from the medical records of 97% of all cancer patients. Data from these patients is effectively siloed in systems maintained by the individual organizations diagnosing, delivering and arranging the finances for the care. The data resides in the patient chart, not just the claims data. 18

The Future Value of Healthcare Big DataUS Cancer Specialist19

The value of Big Data is exponential. The worldwide healthcare big data market, is slated to grow at a 17 percent compound annual growth rate to reach more than $10 billion over the next 4years, Tech and Pharma both see the value here. Google Ventures dropped 130M into an Oncology Analytical Platform Company startup Roche pharma just contributed 175M this year to the same company. So we can easily see, healthcare data extends into all business segment interests.


Advanced Analytics

Predictors for readmission/recurrencePredictors for bad outcome/ongoing treatmentIncentives for better health choicesWhat program/pathway leads to the best outcomeClinical Analytics

Key Health Indicators across patient/member populationProvider Quality ScoresTotal Cost of CareProductivity and Resource Utilization The Value is in the AnalyticsUS Cancer Specialist20

So where does the value reside, primarily in Clinical and Advanced analytics, which have the ability to transform healthcare.Clinical analytics bring together clinical, financial and operational data to answer questions and perform retrospective analysis about how healthcare organizations are running, the state of their patient populations and the effectiveness of programs. Advanced analytics, which are more predictive and forward looking in nature, often focus on making predictions regarding at-risk patients. For example, they might help providers identify which patients require immediate intervention or additional treatment, or which patients could most benefit from particular wellness programs.These key analytics go far beyond todays static reporting architecture, focused more towards Business Intelligence and Artificial Intelligence to provide a higher level of clinical care. 5/13/16US Cancer Specialist20

US Cancer Specialist21Calculating Actual Cost of Care

Select the medical conditionDefine the care delivery value chainDevelop process maps of each activity in patient care deliveryObtain time estimates for each process.Estimate the cost of supplying patient care resourcesEstimate the capacity of each resource, and calculate the capacity cost rate.Calculate the total cost of patient care.

Opportunities to Improve Value Exist Today with various analytical tools available. The process however is somewhat manual, and actually consists of actively engages physicians, clinical teams, administrative staff , and finance professionals in creating the process maps and estimating the resource costs involved in treating patients over their care cycle. The benefit this has is bridging the divide between clinical and business leaders to address cost-cutting steps.With good estimates of the typical path an individual patient takes for a medical condition, providers can use the time-driven activity-based costing (TDABC) system, which is now part of most analytical cost modeling platforms, to assign costs accurately and relativelyeasily to each process step along the pathTDABC builds a common information platform that will unleash innovation based on a shared understandingof the actual processes of care.Capitalizing on these value-creating opportunitiespreviously hidden by inadequate and siloed costing systemsis the key to solving the health care cost problem.One Key Value of this model is to Eliminate unnecessary process variations and processes that dont add value.

Outcomes: Outcomes -shouldbe measured along multiple dimensions, includingsurvival, ability to function, duration of care, discomfortand complications, and the sustainabilityof recovery. Better measurement of outcomes will,by itself, lead to significant improvements in thevalue of health care delivered, as providers incentivesshift away from performing highly reimbursedservices and toward improving the health status ofpatients.

Costs: While measuring medical outcomes has receivedgrowing attention, measuring the costs required todeliver those outcomes, the second component of thevalue equation, has received far less attention. In thevalue framework, the relevant cost is the total cost ofall resourcesclinical and administrative personnel,drugs and other supplies, devices, space, and equipmentused during a patients full cycle of care for aspecifi c medical condition, including the treatmentof associated complications and common comorbidities.5/13/16US Cancer Specialist21

Customer Focused Big Data

Personal Cancer ExperiencePersonal Cancer OutcomesMolecular and Clinical Shared DataComprehensive Picture and Management of the Disease by the Patient


So to this point, we have discussed the financial and clinical quality benefits of Big Data. But what we havent included is the patient experience portion of the equation. Incorporating the patient experience, more precisely their personal experiences with the Care Pathway and Care Team and their actual side effects and outcomes, and combining that with the collection of their personalized molecular data, brings together everything needed to better understand the patient, identify unmet needs, and use that insight to develop better technology, treatments and care protocols.This type of data will hopefully transcend the existing Care Pathway for physicians and patients to make a treatment decision, more in tune with the patient life goals and treatment preferences. 22

Analytical Resource ToolsUS Cancer Specialist23Enables insight into:RiskActual care costsOutcomes AttritionResources ReferralsPerformance Population HealthReadmissions/Recurrence

To Enable the Insight into the Future of Healthcare that Big Data holds, advanced analytical resource tools need to be more fully adopted and integrated within oncology practices.As the healthcare industry transitions from a fee-for-service to a fee-for-value payment model, providers are increasingly taking on the financial risk of poor patient outcomes and are incentivized to deliver efficient higher quality care at lower costs across an entire patient episode.Clinical Variation Modeling or Management for example draws on the power of machine intelligence to rapidly analyze all your electronic medical record (EMR) and financial data, representing thousands of patient procedures and millions of individual events. It automatically surfaces groups of similar patient procedures and generates clinical pathways that result in the best patient outcomes at the lowest costs for your local patient population.5/13/16US Cancer Specialist23

Tracking, tracing and monitoring of patients and staff.Tracking and tracing medical devices and equipment.Tracking and tracing products and materials.Tracking and tracing workflows.Big Data on the MoveUS Cancer Specialist24

Radio-frequency Identification Technology has gained momentum within healthcare institutions, but still shows opportunity for growth, with only an estimated 10% of healthcare facilities utilizing the technology. The question, isnt necessarily the capability of the technology, but rather the cost to implement, upwards toward 600k for a medium size facility. The benefits of RFID technology have been shown to extensive cost savings through better control of hospital productivity, asset management, cost performance, financial performance, and supply chain performance. Clinically, the technology has increased efficiency and productivity; and increased patient safety and satisfaction.This technology is available today and has been implemented successfully in Cancer Centers within the US and abroad. It is for this that Automation, Efficiency, Accuracy, and Integration play a great factor in collecting beneficial Big Data for Oncology Centers from a Quality, Care, and Financial aspect

24

Future of Tumor Registry DataUS Cancer Specialist25

The future of the Cancer Tumor Registry holds greater value than todays model if we can actively engage Bid Data Analytical Tools to project an Interactive Knowledge Base. Todays Tumor Registry, holds extensive amounts of historical data, which is most commonly associated with Cancer Statistics within the US and Internationally. Tumor Registrars have been challenged to easily connect the patient with all the care points within and outside of the health system due to lack of interoperability between reporting platforms as well as providers outside of the main health system. Therefore, data has served more as a reporting structure verse an Interactive knowledge base to assist care providers in making real time decisions. So where are we headed. The future of the Tumor Registry lies within that Centralized Interactive Knowledge Base platform, gaining real time data from multiple various data platforms from basic EMR data, to Clinical Research, to real time outcomes/side effects of similar patients. This centralized platform has the ability to:Unlocks, assembles, and analyzes cancer patient medical recordsUncoverspatterns that can improve patient careAllowsdoctors to compare their care against guidelines and the care of their peersProvide guidance by identifying the best evidence-based course of care25

Challenges Faced with Big Data Expansion26US Cancer Specialist

Of course to achieve any gain, there must be certain challenges that pose a barrier to Big Data Expansion. I will look to cover what I believe are perhaps the top 5.


US Cancer Specialist27Unclear Vision and ApproachData driving the End-Goal or The End-Goal searching for Data Support

One of the most common challenges organizations face when trying to exploit the benefits of big data, is lacking a shared understanding of the companys Vision and Approach, as well the role of data in reaching those targets.There might be conflicting views on which end-goals the data initiative should drive, what are the required investments, and who should be responsible for what. This kind of misalignment within an organization tends to make big data initiatives scattered and ineffective.Should an organization solely rely on what is available to set-forth its strategy or should the organization search for the additional data support to meet that strategy. Too often it is the former, whereas those with a sound vision ahead rely on the latter.


US Cancer Specialist28Data SecurityLack of robust, integrated security.

111 Million Patient Records Hacked in 2015*98% was due to a large-scale cyber attack.*56 Breaches in 2015 vs. 31 Breaches in 2014.*81% of Healthcare Systems have had at least one cyber attack.150% state adequately prepared to handle attack.1*Bitglass 2016 Healthcare Breach Report12015 KPMG healthcare cybersecurity survey.

Undoubtedly you have heard about the recent attack Hackers have turned to, locking out health systems of their EMR via a Ransom Ware program, which is reportedly a simple cyber attack, in exchange for an electronic Bitcoin payment. This goes beyond what other expert hackers have been able to do, the direct access to the patient medical record and other personal/financial information stored within the the health systems EMR. The numbers you see here are rather disturbing and paint a picture of what may still lay ahead for years to come. Health Systems and Providers alike have spent extensive amounts to invest in EMR platforms, storage, connectivity, but still lack to provide the needed infrastructure to protect the data. The fact is, most of healthcare simply doesnt spend enough on IT data security. Ina study conducted by HIMSS Analytics and Symantecthat polled 115 IT and security professionals in hospitals with more than 100 beds, more than half (52 percent) said their organization dedicated between zero and 3 percent of the IT budget to security. Just 28 percent said they spent between 3 and 6 percent of IT budget on security.This perhaps remains one of the greatest barriers to patients fully opening up to the Electronic world of Health Data, and from my experience, one of the greatest concerns when it comes to patients actively wanting to engage in Patient Portals for example. 5/13/16US Cancer Specialist28

Patient Privacy vs. Public Good

Data SharingJAMA.2016;315(10):1051-1052. doi:10.1001/jama.2015.19426.41/211 available apps* for Diabetes Management had Privacy Issues.4/211 asked for permission to SHARE Data.Half Stated they would Collect Data16/211 Stated they would use data for Advertising.6/211 Stated they Would Not Collect Childrens Personal DataUS Cancer Specialist29*Android Apps in Google Play

Data Sharing is not to be outdone by Security when it comes to patient concerns for privacy. Unquestionably, Big data has shown good outcomes for medical science, but potentially risky for the patient. By amassing and analyzing massive quantities of digital information from multiple sources, including an emerging class of wearable devices and smartphone apps, medical professionals will be well equipped to solve major health problems and warn people of emerging health threats as well as preventive measures. But big data's role in healthcare may be hindered by government privacy regulations such as (HIPAA)the regulatory process over the security and disclosure of personal health information by health insurers, medical care providers, and other entities. The government had much more control over the paper medical record of the past, and to an extent the modern day EMR. But with the advent of Mobile Health Technology and Apps for every operating system platform, patient privacy has become even more a sensitive issue, as data is being collected for not only clinical purposes but also marketing as well. A recentstudyin the Journal of the American Medical Association (JAMA) found that 41 apps out of 211 available for managing diabetes in Google Play had privacy policies. Only four asked users for permission to share data.More than half the apps with privacy policies stated they would collect data during use or registration. Only six of these apps explicitly stated they would not collect children'spersonal data; 16 said user data might be used for advertising purposes.So as not to leave those individuals out who may not care as much as about their privacy versus making a buck off of it, a few startups have been established which allow patients to sell their own personal data in an aggregated and anonymous format to those entities looking to purchase such information. 29

InteroperabilityImplementing data interoperability requires realizing data integration and data exchange as well as enabling effective use of the data that becomes available.US Cancer Specialist30A typical Health System can operate with 10-300 Different Systems

Interoperability, is second only to Meaningful Use as a primary concern of providers when it comes to an Electronic Medical Record, which directly coincides with the collection of Big Data. Additionally, the problem interoperability and coordination of information has placed on the patient should not be taken likely. All too commonly, data interoperability, data integration and data exchange are confused, possibly because they share some commonalities in terms of issues and goals. Individual Health Systems can operate with 10-300 different level Patient Record or Data Systems, and hospital and provider mergers have not made this any easier. One commonality is that all of these systems connect to the patient, but do not connect to one another. Improving Interoperability goes beyond a Health Information Exchange or the government mandated Consolidated Clinical Document Architecture (CCDA). To get to health information interoperability, we need more than just transport standards: we must also use standards for vocabularies and terminologies (to help standardize the meaning of the words that we use), standards for structure (so EMRs know how to break a message into the appropriate information chunks). Interoperability means I can access information that you have, and I can use it, I can change it, I know where it comes from, I know whos responsible for it. And you can do the same.Overall, Interoperability has caused providers and patients issues relating to continuity of care, duplication of medical services, and increased healthcare costs. But from a Big Data perspective, Interoperability has caused disruption for accurate collection and aggregation of patient medical data. Which leads us to our final top 5 concern. 5/13/16US Cancer Specialist30

Bigger, Better, More Insightful? Rural HospitalCommunity HospitalRegional HospitalAcademicAccuracy of Cancer Big DataNo matter the size, accuracy comes in what is being actually analyzed; and when inaccurate, it is corrected for accordingly.

Finally, Accuracy of Cancer Big Data or the Veracity of the Data. Big datamay hold a lot of potential, but it can still be held back if the data being analyzed is inaccurate. Due to restrictions on technology and other business considerations, the analyses companies are getting back may not reflect what is really happening. If businesses want to ensure their big data insights get the desired results, they need to improve the accuracy in their analytics efforts.So regardless of the size of the data being collected, it first and foremost has to be accurate, whether electronically or manually obtained. That big data collection then has to be analyzed for accuracy, all of it, only looking at part of the data could lead to the forming of the wrong business or clinical conclusions. 31

US Cancer Specialist32ConclusionBig Data is built upon Volume, Velocity, Variety, and Veracity.Predictive and Prescriptive Analytics are driving the future vision of Healthcare Big Data.Structured Data is the most Common Big Data in HealthcareUnstructured Data holds most Clinically Valuable Healthcare DataHealthcare Improvement is geared toward Cost Effective, High Quality Patient Centric Care.Clinical and Advanced Analytics can drive Quality of Care within Healthcare, providing cost savings in excess of $300M annually. Healthcare IT Interoperability is the key barrier to accessing Big Data.

In summary, I would like to finish by saying that Big Data within Oncology has a significant potential for Clinical and Business Discoveries that can change the way we deliver care today. It will however take a unified effort to collect, analyze, and predict which direction that will take us. It is key to understand and set-forth that common vision of Big Data early on to make best use of the financial resources needed to secure the top analytical reporting tools and the staff needed to achieve the desired outcomes. 5/13/16US Cancer Specialist32

US Cancer Specialist33Thank You! Email: [email protected]: 727-487-2840Web: uscancerspecialists.com
