Ever increasing computational power, advances in artificial intelligence and lower computing cost (because of cloud computing services such as Azure and Amazon Web Services) has enabled healthcare systems – often laggards in quality improvement and technology adoption – to rapidly implement analytics systems. Such systems enable enterprises to analyze and model their processes, engage in meaningful quality and process improvement activities, and prepare to succeed in value and risk-based payment models.
Hewlett Packard Enterprises recently published a piece that delineated some of the benefits that enterprises can gain from analytics (specifically the predictive form) –
- Reducing readmissions
- Gauging operating room (OR) demand
- Better manage supply chains
- Staffing optimization
- Intervening with care pathways prior to adverse events occurring.
Even enterprises with existing, legacy analytics systems – for example, those that mainly work with claims-based data or lack predicative or real-time capabilities can obtain the above efficiencies with enhanced analytic capability.
Modern data warehouses and other analytic tools
A modern data warehouse must be flexible, SQL-enabled, cloud-based, and highly secure. Snowflake Computing’s cloud-based infrastructure is an example of one such system which can be easily scaled as it is offered to clients with usage-based pricing. A data warehouse alone, however, is not sufficient for an enterprise. Tools must be provided to prep, transform, and perform analysis on the data.
Alteryx Designer, one such tool, allows analysts to prep and blend data from heterogeneous sources – e.g., CSVs, databases, Excel files – in an efficient and reproducible manner, and, more importantly, it includes spatial and predictive analytics. This enables organizations to move from retrospective and barely actionable data to immediately actionable real-time predictive analytics.
Key strategies for system implementation
The implementation of an analytics system (or the migration of a legacy system) is not a project to be undertaken without serious thought how change is managed within an organization. Many facets of an organization will be impacted by such projects. Matthew Morris, Lead Data Enabler at an international wholesaling club based in Washington State who has overseen both the maintenance of legacy analytics system and the migration to a modern one using a team from Decisive Data and Alteryx’s tools noted some key behaviors or strategies that should be taken to ensure a successful project:
- Get leadership buy-in – Many people naturally resist change. Ensuring that leadership across the enterprise is committed to the change will enable a coherent messaging to be addressed to all stakeholders. There are many strategies to achieve leadership buy-in. A notable one, the ADKAR model is described here.
- Choosing an effective partner – Especially for mission-critical or strategically sensitive projects, external help is critical. Talented consultants can augment staff skill shortages and bring critical experience (and lessons learned from other projects).
- Be present – Project leaders should spend time onsite at the various locations where work occurs to ensure proper training and data conversion. During training, don’t just rely on classroom style training; rather, sit down with users and work through actual day-to-day problems.
- Train Superusers – A successful analytics system empowers users – especially key super users – to use the application on their own and not to depend on report requests to an analytics department. Consider also setting up open office hours where super users or hired technology partners can guide users through specific day-to-day processes.
- Be honest and use humor – The latter can assist in convincing people to give a new system a chance. Honestly builds rapport within an organization especially during a challenging project. If one is converting from a legacy analytics system to a new one, it is important to empathize with users. They have been doing their work on the old system for years; their apprehension is natural.
- Make friends with problem persons – Try working alongside so-called problem persons. It will help you as a project leader to determine why they are negative and show that you are empathetic to their concerns and are personally invested in their successful transition. Note, however, there will be a minority of users that will refuse to accept the change. For the project to be successful, it may be necessary to move on and hope that they come around once the project is successful.
- Be a warrior and ignore borders – Sometimes it is important to put a stop to delaying tactics such as an abundance of meetings and just move forward. Additionally, such assertiveness must be used to modify the scope of the project if it is necessary to keep the organization functioning.
- Be present after go-live for project clean-up – Equally important to effective management during the project itself is how the post-production period is managed. Consider personally walking through each office or cubicle and talking to each user to see how they are using the new system and, if needed, remediate any knowledge gaps or leftover issues. MindTools has an excellent article that further discusses the need for post-implementation reviews to ensure that the delivered project works.
Morris’s tips for a successful project can be summarized in two simple points:
- Spend your time with the users
- Remember that the project plan is not the law, it is a guide; scope creep is a concern but ensuring organization buy-in is more often than not worth the risk. Remember,
The project plan exists for the organization, not the organization for the project plan.
The bottom line
Using these tools and methodologies will help healthcare organizations successfully implement or migrate analytics systems. As their users become more confident, integrating the advanced analytics tools into their daily work, they can expect that more process and quality improvement initiatives will be undertaken.
As a result, the organization will become more welcoming and less apprehensive about making changes whose success is dependent on sophisticated analytics, such as alternative payment arrangement (e.g., bundled or capitated payments). Decision makers and analysts will know that they can rely on the use of real-time predictive tools to achieve needed cost and quality goals.