HCD WP 2011 TipsForSuccessHowToImplementAndLaunch

7/30/2019 HCD WP 2011 TipsForSuccessHowToImplementAndLaunch

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Tips for Success:How to Implement and Launch an EDWto Achieve Faster and Better Results

Written by

Herb Smaltz, Ph.D., FACHE, FHIMSSCo-Chairman and CEO Health Care DataWorks


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way to create on-demand, cross-departmental or enterprise-view reports for the board of directorsor the CEO. More often than not, organizations expend an incredible amount of manual labor topull data from the various sources to put together board books. The following month, the army of analysts does it all over again. Below the C-level, managers do what they can to pull together datafrom disparate sources also via largely manual efforts. The graphic in Figure 1 is a representationof what we find at most hospitals and health systems:

A plethora of competing, disjointed reporting tools Data governance not mature enough yet to deal with enterprise approaches

to data management Lack of true understanding by the C-suite of what it takes to accomplish enterprise

business intelligence and reporting in a real-time, automated way

Figure 1 Depiction of BI & analytics at most hospitals and health systems


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there tend to be

pockets of expertisethat understandslices of theenterprises dataand information,but no one or nodepartment that fullyunderstands all of the enterprises dataand how to use it

cross-functionallyto better informmanagementdecision making.

How we got here

It is also quite understandable how such a state of datafeudalism evolved at many hospitals and health systems.

As the industry began to automate via specific, oftendepartmentally focused applications, it simply leveragedwhatever reporting capabilities came with the departmentalapplication. Today most hospitals of any size have dozens or even hundreds of unique transaction-based applications tosupport a myriad of operational functions. Unfortunately,these applications have highly heterogeneous data structuresmaking it incredibly difficult to leverage this siloed data across

applications. For instance, assessing the impact of cost controlson quality outcomes, a very important current-state dataanalysis need, requires merging data from disparate systems.

Exacerbating the difficulty of creating a more consistent, reliabledata infrastructure are issues of departmental data ownershipor stewardship (as opposed to an enterprise approach to datamanagement): each department wants to retain what it hasalready created (via vendor-provided tools used to createdepartmental data marts, spreadsheet-based scorecards,spreadmarts, home-grown databases, etc.) without having tochange to meet the broader needs of other departments or theenterprise as a whole.

The costs of the status quo

The true labor costs of hand kludging this data into presentable exhibits is shocking. Figure 2presents an analysis of labor costs incurred by organizations that have not invested in an

enterprise BI capability, such as a true enterprise data warehouse (EDW), compared to those thathave. The organization depicted in the top portion of Figure 2 estimated that it created 1000reports, spreadsheet graphs, or other data exhibits in the course of a year. It further estimated thatindividuals in its various functional departments spent between 40 and 200 hours to create eachreport. At an average labor rate of $50/hr., this organization was expending at least $10M annuallyin labor associated with finding, gathering, staging, integrating and building reports and graphics.


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On the other hand, the organization depicted in the bottom portion of Figure 2 had invested in anenterprise data warehouse, and managers throughout the organization could quickly leverage theEDWs integrated data to create reports and or graphics in 30 minutes to 10 hours each. Moreimportantly, the managers at the EDW-enabled organization made comments such as, The EDWhas enabled me to do the job I was hired to do; before, I was collecting data; and I used to spendhalf of my time each month hunting for and hand massaging data; now I have time to focus on myreal job as a nurse manager.

Figure 2 Labor costs associated with building reports, graphics and exhibits

The path forward: Enterprise BI

To overcome the labor-intensive status quo and achieve these kinds of enterprise efficiencies andeconomies, what is needed is something simply stated in a famous line from a Star Trek movie.Mr. Spock, when asked why he was sacrificing himself to save the starship Enterprise, replied toCaptain Kirk, The needs of the many outweigh the needs of the few. What is becoming clear to


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enlightened leaders in hospitals and health systems is that in order to overcome this departmental

feudalism, healthcare delivery organizations must treat information as a strategic, enterprise asset just like their peers in other information-intensive industries such as financial services and retail.While some might call for more training or for vendors to make the reporting capabilities of their information systems more interoperable, organizations cannot avoid investing in people, processesand enterprise capabilities that are expressly focused on levering enterprise-wide information if

they truly want to achieve and sustain superior clinical and business results.

Lessons from the Trenches

Leading practices in creating and sustaining an enterprise BI capability

While there are many ways to create enterprise reporting and analytics capabilities, we find the

following common success factors at leading hospitals and health systems:

Creating an enterprise BI/reporting/analytics group or department a BI competency center Integrating siloed data sets into a true enterprise data warehouse capability Creating an enterprise level data governance committee or capability Measuring the benefits realized from investments in enterprise BI

Creating a BI competency center

An enterprise BI competency center is a formal team with enterprise-wide responsibility for delivering enterprise BI capabilities. Considerations for various alternatives to stand up theBI team includes the following:

Degree of parochialism with respect to data ownership(that could detract from an enterprise approach)

Depth of skills within key data steward departments(finance, quality, IT, materials management, etc.)

Organizational policies or norms related to adding permanent head count Degree of EDW and other BI skills within your organization Desired speed to value delivery View of BI as a new core competency vs. BI as a new cost center


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These considerations typically lead to two basic templates for standing up an enterpriseBI/reporting/analytics group. One approach is to staff the new department/team/group with mostlynew hires. The other approach stands-up the enterprise BI group with a minimal number of newhires. Each approach has unique pros and cons, which are articulated in Table 1 on the next page.

Figure 3 Alternatives for staffing an enterprise BI department/team/group


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ensures departmental buy-in to the new enterprise group by virtue of having known analysts thathave already proven themselves. In making this move, these organizations send a strong signalthat departmental, siloed approaches to analytics are being dismantled in favor of creating anenterprise infrastructure for reporting and analytics. This doesnt mean that individual departmentswont have their own analysts they certainly will. However, instead of the departmental analystshunting for and hand massaging data, they will instead consume ad hoc capabilities as well asstandard reports and dashboards that are being managed by the enterprise BI group.

Integrating siloed data into a true Enterprise Data Warehouse

Much has been written about alternatives to building an enterprise data warehouse. Some writerssuggest that there is no need to integrate siloed data into a true EDW; they say the heterogeneous

data sets can simply be linked in some federated manner. While this is a quicker approach toallowing a core group of data analysts and researchers access to the various data siloes, our experience suggests that this approach makes it incredibly difficult to get information into thehands of the majority of people who need it within a healthcare provider organization. Often,federated models work well for small, focused groups, but create organizational bottlenecks asmore and more users within the organization want to leverage the data. It is beyond the scopeof this white paper to plunder the depths of the academic literature on this subject. The mostpragmatic way to approach this topic is by examine the desired end goal of the enterprise datawarehouse. We suggest that organizations do best when they strive to manage via informationand do so by providing relevant and timely self-help information to the lowest level possible inan organization . This requires a true enterprise data warehouse with a unifying data model thatessentially takes the heterogeneous nature of the data and homogenizes it for self-help useamong the organizations information consumers.

Converging the multiple data silos into a unified EDW, as depicted in Figure 4 on the next page,offers the following benefits:

Provides self-help dashboards/scorecards to empower employees to manage by information Creates a single version of the truth a single, trusted source Provides both standard and ad-hoc reporting capability Reduces privacy risks via enterprise de-identified data Creates the foundation to move from retrospective analytics to predictive analytics


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Figure 4 Merging siloed data into an EDW with a unified data model

By thoughtfully understanding the original source of the data and how the organization wants tobring it into a unifying data model (e.g., which source system is the best source of data on patientdemographics), and by merging previously siloed data sets (e.g., bringing EHR data together with ERP data), organizations are beginning to move from a departmental data capability to atrue enterprise data management capability a must have core competency in the post-

ARRA/HITECH Act world of reduced reimbursements and increased transparency. Making thischange, however, means that another important new process must be put into place enterprise

data governance.

Creating an enterprise-level data governance committee or capability

When departments had their own spreadsheet-based scorecards, data marts or databases tosupport their analytic needs, decisions relating to how certain key performance indicators shouldbe calculated were largely left to the individual departments. Additionally, typically these


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departments were largely responsible for how individual source applications (e.g., billing system,registration system, emergency department system, etc.) were implemented, including which fieldsshould be required data input fields. For example, it is not uncommon for a registration departmentstaff to be instructed to leave certain unimportant fields blank to speed the registration process.But later, when registration data is merged for the first time to report on, for instance, referringphysician patterns, the department managers are often surprised that the physician or physiciangroup with the highest number of referrals is blank.

Enterprise data governance, like any other form of governance, requires that a representativegroup of stakeholders be charged with ensuring that the information needs of the entireorganization are being addressed. The figure below provides a sample committee structure to

oversee both the development of the organizations enterprise data warehouse and enterprisedata governance.

Figure 5 Sample enterprise data warehouse and data governance committee structure


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In this sample committee structure, the senior-level committee (the EDW Steering and DataGovernance Committee) is typically represented by the senior-most leaders of the organization(e.g., the senior officers from Finance, Marketing, Strategy, Medicine, Information Systems, etc.).This is important to ensure that initial efforts on the EDW are prioritized with a group enterpriseperspective. Additionally, as data related issues bubble up from the subordinate data governancesubcommittee (e.g., how census should be calculated, how cost should be attributed to an OR,whether or not to require previously blank fields in a particular source system to becomemandatory fields for the benefit of more robust analytics, etc.), it is important that the senior committee is represented by buck stops here decision-makers to expedite the process.

With this committee structure in place, the two subcommittees can focus on implementing the

EDW and ensuring that the data that is loaded into the EDW and made available to end usersis of the highest quality. Over time, the work of the EDW implementation committee may wane andonly focused, ad hoc teams may be formed to lead new EDW initiatives. However, the work of thedata governance subcommittee is never done. As new source systems are acquired, old ones areretired, etc.; the work of the data governance subcommittee must go on in order to assure that thequality of the data in the EDW remains at the highest standard possible.

Of course, there can be a number of alternatives to the committee structure proposed above.For example, some organizations may choose to have two totally separate and stand-alonecommittees: one to oversee the EDW prioritization and another to deal with data quality anddata governance issues.

Measuring the benefits realized from investments in Enterprise BI

Finally, it is a leading practice to actually track the benefits achieved from investing in enterpriseBI resources (such as establishing an EDW and creating an enterprise business intelligence or analytics department). It is important to note that investing in BI capabilities will not provide resultsalone. Creating the BI resources discussed in this white paper is a necessary but insufficient

condition for actually achieving results. What must also be present is management that is focusedon data-driven performance improvement. In other words, having a quality dashboard that presentsdozens of key performance indicators will have little effect without senior leadership that isassessing the KPIs regularly, as well as managers who can drill down to their particular area of responsibility to understand the root causes of good or bad performance and who are heldaccountable to take action to constantly improve performance.


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With this kind of a management mindset, it is useful to track particular focus areas. For instance, if length of stay has been identified at the beginning of a fiscal year as a target area for improvement,track the length of stay overall as well as by unit. Assign a monetary value to what one days stayrepresents in costs to the organization. In addition, calculate what having a bed open for a newpatient one day earlier would represent in new revenue. Present these results to senior leadershipover time. In this way, an organizations leadership becomes keenly aware of the return oninvestment they are gaining from both the investments in the BI infrastructure and their continuedmeasured management focus on continuous improvement.


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About the Author

Herb Smaltz is responsible for leading the strategic direction of Health Care DataWorks (HCD).He is one of the companys founders and has positioned HCD to be the leader in providingbusiness intelligence solutions that empower healthcare organizations and health systemsto improve their quality of care and reduce costs.

Smaltz has more than 21 years experience in healthcare management. Prior to founding HCD,Smaltz was the CIO of The Ohio State University Wexner Medical Center, a $1.7 billion, six-hospital academic medical center comprised of more than 1,100 beds and 13,000 full-timeemployees. In that role, he led an IT organization of 255 individuals with an annual operating

budget of $42 million. In addition, he served as an Associate Vice President for Health Sciences,leading collaborative initiatives between the three mission areas of the medical center: research,academics and patient care.

Smaltz is a Fellow of the Healthcare Information & Management Systems Society (HIMSS) andserved on the HIMSS Board of Directors from 2002-2005 and as the HIMSS 2004-2005 BOD ViceChair. In addition, he is a Fellow in the American College of Healthcare Executives (FACHE).

Smaltz holds a bachelors degree in management information systems from the University of Tampa, a masters degree in business administration from The Ohio State University, and aPh.D. in Information & Management Science from Florida State University.

About Health Care DataWorks

Health Care DataWorks, Inc., a leading provider of business intelligence solutions, empowershealthcare organizations to improve their quality of care and reduce costs. Through its pioneeringKnowledgeEdge product suite, including its enterprise data model, analytic dashboards,applications, and reports, Health Care DataWorks delivers an Enterprise Data Warehouse

necessary for hospitals and health systems to effectively and efficiently gain deeper insightsinto their operations. For more information, visit www.hcdataworks.com .


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Contacting Health Care DataWorks

Published: July 2011

2012 Health Care DataWorks, Inc. ALL RIGHTS RESERVED

Phone

1-877-979-HCDW (4239)

Email

[email protected]

Address

1801 Watermark Drive

Suite 250

Columbus, OH 43215

Web

www.hcdataworks.com

HCD WP 2011 TipsForSuccessHowToImplementAndLaunch

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