Healthcare Reporting: Centralized vs. Decentralized презентация

organizations look to get more deeply into data analytics is whether their reporting functions should be centralized or decentralized.
The answer I usually give is:

Reed Hastings, co-founder and CEO of Netflix, refers to as “highly aligned, loosely coupled.”
Highly aligned means everyone shares the same strategic goals–team interactions are focused on strategies.
Loosely coupled means different groups have the flexibility to approach tactics differently as needed.

team.
The coaching staff develops a game plan and the overall strategic approach the team will take to win the game.
However, during the game, the offensive and defensive coordinators call the plays according to the game plan.
That is highly aligned.

certain defensive formation and realizes there is a better opportunity available.
He has the freedom to call an audible and make a tactical decision that is still highly aligned with the team’s overall strategy.

create insights that help achieve strategic goals.
Analysts are the conduits that collects various data points from different sources that allows non-analysts understand and act on it.
The tactics they use depend on the organizational structure.

approaches to analytics and reporting.
One is a centralized model, where the analytics group is its own entity, independent of any particular group.
In a decentralized model, the analysts work directly for the different groups or departments.

pros and cons; the pros of one are usually the con of the other.
Let’s look a little more deeply at each to see where their strengths and weaknesses lie–and why a hybrid of the two tends to work best.

had to choose a single approach, this would be recommended based on our experience with both.
The strengths of the centralized model follow:

with standard tools. If everyone is sharing the same tools, the analysts are able to share their knowledge about those tools as they learn.
Having a standard set of reporting tools ensures it can be done almost instantly.

where and when they’re needed; those with a decentralized approach cannot.

model, the organization can have a mix of skill levels since the junior or mid-level people can always go upstream to get questions answered or learn new skills.

what is learned in the domain tends to stay in the domain.
When reporting is centralized the organization can aggregate reports from multiple areas and build customized dashboards that enables executives to get ahead of trends.

provided by experts in analytics who know how long a particular task should take–and when a heroic effort is needed to deliver a particular report.

departments or higher-level initiatives usurp the needs of another department.
A dissatisfied department Might say, “Fine, if IT can’t help me, I will go hire my own people.”
This common attitude is typically what drives organizations to go to a decentralized model.

an either/or choice.
Organizations with the most success are the ones that combine the two to keep their reporting processes highly aligned yet loosely coupled – giving them the best of each while overcoming each model’s negatives.

focused on a specific need.
With a lot of low-hanging fruit out there it’s easy to take advantage of immediate opportunities.
If their work aligns with the overall organization’s strategy they don’t need to follow the formal processes that are part of the centralized model.

Hospital Reports Bobbi Brown, Vice President of Financial Engagement
Physician Reporting: The Secret to Useable, Engaging Reports Dr. Ed Corbett, Medical Officer
Data Analyst Saves Hospital Millions John Wadsworth, Vice President, Client Engagement
4 Ways Healthcare Data Analysts Can Provide Their Full Value Russ Staheli, Vice President, Analytics
Self-Service Hospital Reporting Possibilities Brian Eliason, Vice President, Client Engagement and Kristi Mousel, Business Intelligence and Data Warehousing Professional

Link to original article for a more in-depth discussion.
Healthcare Reporting: Centralized vs. Decentralized

Russell Staheli joined Catalyst as a data architect in October 2011. He started his career as an Intern and later Outcomes Analyst at Intermountain Healthcare in the Institute for Health Care Delivery Research supporting the Advanced Training Program for Executives & QI Leaders (ATP) and the Primary Care Clinical Program. Before coming to Catalyst he worked as a Management Engineer Programmer Analyst for the Duke University Health System in their Performance Services department supporting their Infection Control and Epidemiology efforts.
While there, he also worked as an external consultant to advance the analytical work of the Duke Infection Control Outreach Network (DICON), a collaborative of over 30 community hospitals. Russ holds an Master of Public Health in Health Policy and Administration from University of North Carolina Chapel Hill and a Bachelor’s degree in Health Services Research from the University of Utah.