Ever increasing computational power, advances in artificial intelligence and the lower of the cost computation (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. To know more, visit the post.

1. The Role Of Analytics in Healthcare
Ever increasing computational power, advances in artificial intelligence and the
lower of the cost computation (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.
Enterprises with existing, legacy analytics systems – for example, those that
mainly work with claims-based data or lack predicative or real-time capabilities
can likewise obtain the above efficiencies. A modern data warehouse must be
flexible, SQL-enabled, cloud-based, and highly secure. Snowflake Computing’s

2. 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.
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 behaviours 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 there and integrate training creatively –

3. 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. Consider also setting up open office hours
where super users or hired technology partners can guide users through
specific day-to-day processes.
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.
Be honest and use humour –
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 but acknowledge that not everyone will
accept change –
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

4. 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.
Matthew Morris’s tips for a successful project can be summarized in two
simple points 1.) spend your time with the users – be present and 2.)
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. The project plan exists for the organization, not the organization for the
project plan.
Healthcare organizations undertaking an analytics project using the tools and
methodologies described can, as their users become confident in integrating
the advanced analytics tools into their daily work, expect that more process
and quality improvement initiatives will be discussed and undertaken. The
organization will be more confident – and less apprehensive – about
alternative payment arrangement opportunities such as bundled or capitated
payments because decision makers and analysts will be able to use real-time
predictive tools to achieve needed cost and quality goals.
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