1. Data governance for health care providers Why, where and how? Presented by: Katerina Andronis and Kevin Moysey Data Governance 2012 March, Melbourne

2. Sustaining information quality Measurement and monitoring Environment and culture Strategy and governance Basis of informal research conducted Strategy We have defined and implemented an information quality strategy designed to manage information as an asset Standards We have defined information quality principles, policies, and standards that are used to guide decisions and actions affecting information quality Management We have implemented a data governance model covering key roles and responsibilities, formalised accountability, established decision rights, and identified channels for management actions related to managing our data Accountability We have defined accountabilities for information quality across all functions throughout the organisation Education We actively educate management and staff regarding data quality and our approach to managing it Process Personnel can readily access the information they need to understand data quality requirements and processes related to their jobs Measurement There are well defined data quality standards, and associated reporting Monitoring Achievement of data quality standards is actively monitored and compliance is an accountable element of job responsibilities Projects Data quality implications are actively and appropriately addressed during computer application implementation or upgrade projects Operations Information quality is explicitly built into our business operations, processes and systems Question responses 1.Haven’t started to do this 2.Have made a start but it’s early days 3.Significant progress but not complete 4.Have largely completed and embedded Definition Data governance is the definition and exercise of authority and control over data assets encompassing the entire data lifecycle(creation, storage, access, use, archiving, disposal). Based loosely on IAIDQ framework 2 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

3. The information challenge for health care providers Activity based funding pays for the health service outputs delivered at an established “ efficient price” which requires diligent counting/billing processes and an accurate knowledge of service cost. So, from a revenue perspective Need to manage patients and services with a view to best revenue alignment and reduced revenue leakage within obligation framework and acceptable practices Need to code promptly and accurately under appropriate guidelines Need to identify and rectify individual DQ problems not “code around” them or rectify on best-efforts basis after the fact. … and from a cost perspective Ideally need to understand cost per service instance Need detailed current service profitability reporting and trended reporting Need to understand cost driver and levers. Then there are the rules Significant funding, regulatory and compliance requirements. Key point Much of the work in these areas is process- and system- related, but sustainable capability and improvement requires the allied data governance to be in place to ensure that data is defined and captured correctly and can be reliably used for accounting, management and analysis purposes. 3 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

4. Health service output functional footprint Many areas are involved directly and indirectly in delivery of funded health care outputs. 4 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

5. Health service output revenue information footprint 5 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

6. Data governance organisation in health care provider CXX Business data stewardship Data steward community Data Stewards Finance processes Governance working teams Patient and clinical care Non-clinical care support Operational support Data owners Health information manager Business manager(s) Operations managers Information technology Facilitate impact assessment and issues management processes Raises Issues and concerns Measure and communication compliance with standards Escalation of issues that cannot be resolved by the Working Team Issue resolution and endorsement of developed governance mechanisms Provides guidelines and task direction Provides executive support and budget approval Provide tools and technology capabilities Request for budget and status reports HR processes Patient care processes Data Ownership functions are expected to have infrequent day-to-day involvement however will be accountable for their data subjects The domain data management function have delegated authority to make agreed decisions as authorised by the data owners Support issue management process Support functions Data quality mgt Change managementIssue management Metadata mgt Business support Education & research Human resources Audit and riskFinance Education & research manager Quality manager Finance manager HR manager etc Governance working teams relate to the information groups from the information model shown earlier. Seek to keep core teams smaller and engage other people as required Data owners should be selected on the basis of relevance to information groups and subjects within them as well as having a suitable level of authority to make and ratify related decisions Existing roles operating with suitable principles, standards and training supported by systems and issue resolution processes Contributes to a body of knowledge Proposes new ideas and policies, standards and processes Work together to develop standards, analyse issues and manage data issues Governance lead 6 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

7. The $64,000 question – justifying the exercise Attempting to justify a complete whole-of-enterprise data governance exercise on the basis of principles will fail (at least experience shows that this is very unlikely to be approved or to succeed). Opportunities to consider include: Responding to a problem that has occurred. The danger here is that this has a strong tendency to remain narrowly focused on a one-time approach to addressing the effect of the problem rather than fixing the cause and preventing further occurrences by institutionalising the changes required Focusing on a single important data subject area and deliberately establishing only a basic capability to contain scope, cost and risk. However, it may still be difficult to justify in terms of benefits unless there significant known issues so focus in information imperative areas Leveraging a systems project that will require data migration to justify the effort required to handle data related work soundly and transition the management capability into business as usual. In other words, incubate the capability in the context of a project and sustain beyond the project Searching through the benefits in business cases of substantial approved projects for benefits that are strongly data-linked and unlikely to deliver the planned benefit unless there is an appropriate effort put into managing associated data quality. This can expose “business value at risk” or benefit shortfall. Generally the cost of addressing the required data governance is much less than the project and can be positioned as a worthwhile supplementary activity Align with process improvement initiatives which almost always have a significant relationship with data. Process improvement is usually short lived or limited if the associated data governance is not also addressed as part of the change. Key point Attach data governance initiatives to something that already matters and is on the executive agenda 7 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

8. Some guidelines for tackling data governance There is no magic bullet for data governance (or the data quality that is a common goal) Have a framework (such as the one we have presented) to provide some context for whatever activity is undertaken so that there is some leverage and convergence over time Build the data governance arrangements into the business as usual organisation or they will evaporate Execute modestly scoped activities in areas that are a clear priority While it is reasonable to have a plan in mind, recognise that you should only move forward at a pace and to an extent where there is support and relevance. Expansion is likely to cease when enough has been done which is certain to be well short of implementing a completely comprehensive approach to data governance across the enterprise Take steps with permanence in mind. A once-of improvement in data quality achieves little over the longer term if the required governance is not assimilated going forward Data governance is not a spectator sport – it is a team participation sport. It can only take place effectively when people understand the purpose, know that they are on the team and know what there role is The adage that “what gets measured gets managed” is true when bit comes to data quality. Even very basic data quality reporting helps instil it as a relevant business activity Keep it simple – for example just go for a basic data dictionary in the first instance Implement the governance organisation as data subjects are addressed Implement data quality reporting as subject areas are addressed. Key point Build to a framework but deploy in small focused steps with an emphasis on ensuring changes, monitoring and management are integral to business as usual. It is better to have modest complete portions than a broad based initiative that remains incomplete. 8 © 2012 Deloitte Touche Tohmatsu Data Governance for Health Care Providers

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