A scenario: your experience, virtual health, and marketing leaders have shaped creative and compelling ideas to personalize interactions with healthcare consumers.

Of course, execution drives those inspirations to fruition, and any type of communication connected to patient data requires a new set of considerations. Realistically, you’ll employ the expertise of development teams as you move your vision toward reality.

YOU MAY ALSO ENJOY – Hip On HIPAA: The Secret Sauce to Successful Marketing Campaigns

An Agile Way of Working With Your Tech Teams

Over the past 20 years or so, how large scale data and software development projects are implemented has transformed from standard Software Development Life Cycle (SDLC) methodologies, such as waterfall and iterative, to a more collaborative approaches like Agile and Scaled Agile Framework (SAFe).

Companies recognize the benefits of Agile concepts and many are on a journey to fully adopt agile concepts. Some currently are using a hybrid approach taking Agile tenants such as Backlog, Sprint Planning, Sprints and daily standups and incorporating that into an iterative/waterfall methodology.

My point here is there are multiple ways to go about executing the delivery of software development projects. However, regardless of the methodology used there is one constant in the delivery equation that never changes…

And that is data.

Data: At the Heart of Progressive Healthcare Interactions

In the healthcare industry there are many rules in place that guide the use and dissemination of data. These rules are defined as part of HIPAA (Health Insurance Portability and Accounting Act) and the use of PHI (Protected Health Information).

One of the most important aspects of working with healthcare data is to ensure all PHI data is masked prior to starting the testing of any logic. This protects the company, employee, and any third party vendors from accidentally violating HIPAA compliance rules regarding the protection of PHI data and facing fines penalties (or even worse).

YOU MAY ALSO ENJOY – [Podcast] Healthcare Data is Changing Consumer Care

Production Tip for HIPAA-Compliant Personalized Interactions

Most companies have a formal process to request the selection of a subset of production data, and the masking of that data for testing purposes. However it needs to be planned for. The securing of masked testing data could take anywhere from 2 to 4 weeks, depending upon the type of data that needs to be selected and the lead time required to coordinate the selection of the required data.

If not properly planned for, this could cause delays in the development and testing of your projects solution.

So remember, no matter what development methodology you employ, take the time to properly plan for the amount and type of data required to successfully test your software logic.

YOU MAY ALSO ENJOY – Hip On HIPAA: How Do We Deliver Better Front-End Experiences

Struggling to Meld HIPAA Compliance and Great Experiences?

We can help. Reach out, and let’s talk.

During the last several blogs, we talked about the Cynefin framework and its four types of projects: Simple, Complicated, Complex and Chaotic.

The Cynefin framework is used to help project managers, policy makers and others reach decisions on how to execute based upon how well you know your end result. The framework consists of five decision making contexts of domains: simple, complicated, complex, chaotic and disorder.

All of which provide guidance and direction for managers to identify how they will need to proceed with execution.

However, do you know which domain all projects start in, that is correct – Disorder.  Until the project manager understands the needs and demands of the project, there is disorder.

The only way out of this domain is to break parts of a project into known domains.  For example if the business leaders cannot agree on the tenants of the project, that would be a good place to start.

For as good project managers we know how to gather requirements. So perhaps we start with a visioning work session where decision makers come together and agree on the vision of the project and several critical success factors of the project.

Once critical success factors are identified, and agreed to, individual use cases can be developed for a specific critical success factor. Now we have clear demands of the project.

By doing this we have partitioned a portion of disorder into a known domain – complicated perhaps, where solid analysis can begin on the defined use case(s).

Research to determine the best technical solution can begin based upon the defined vision, critical success factors and use cases.

To reiterate, Cynefin will guide a project manager to the appropriate domain based upon project need and objectives. The key is to break the project down into small enough components where these components can be isolated and assigned to one of the four known domains.

So far in this blog series I have discussed using the Cynefin framework for providing guidance in determining the best SDLC methodology to use for a particular type of project defined by the framework as well as delving into the Chaotic type of project.

This month I will focus on the Complicated type of projects defined using the Cynefin framework.

As you can see from the picture above, Complicated is the category where good practices can be found. In this category there are multiple right answers, and expert diagnosis is required to figure them out. This sector demands more quantitative approaches such as Six Sigma as an example.

There are several key characteristics which assist in identifying a complicated project.

Characteristics of the Complicated category:

• Multiple right answers are available
• A general idea of the known unknowns
• You know the questions you need to answer
• Don’t know how to obtain the answers
• The problem is more predictable than unpredictable
• Cause and Effect relationship is not immediately known but is discoverable given enough time

If you find yourself managing a complicated project the approaches defined below will help you better define the complex problems.

Approach for Complicated problems:

• Assess the situation and Sense the problem
• Investigate several options
• Analyze large data groups, as needed
• Use experts knowledge to gain insight
• Use metrics to gain control
• Base response on good practice
• Determine a course of action
• Execute the plan, following the Plan, Do, Check, Act cycle

As the picture above indicates, since this is a more quantitative type of project the waterfall methodology can be used with tenants of Agile.

So far in this blog series I have discussed using the Cynefin framework for providing guidance in determining the best SDLC methodology to use for a particular type of project defined by the framework as well as delving into the chaotic type of project.

This month I will focus on the Complex type of projects defined using the Cynefin framework.

As you can see from looking at the picture above, the complex is the category where solutions are discovered by developing a safe environment for experimentation. This experimentation allows you to discover important information that leads to the creation of new emergent solutions.

These problems are always more unpredictable than they are predictable. Hindsight can only tell us if there is a right answer as we explore the problem. Only with detailed experiments, inspections and results will you be able to base your decisions.

The current results are then used to define the next step toward a solution. In such situations, the ability to probe (explore), sense (inspect) and respond (adapt) is critical. There are several key characteristics which assist in identifying a complex project.

Characteristics of the Complex category:

• There are unknown unknowns
• Even the starting point requires experimentation
• The right questions to ask need exploration
• The solution is only apparent once discovered
• The sector of emergence ideas
• Routine solutions don’t apply
• Higher levels of interaction and communication are essential

If you find yourself managing a complex project the approaches defined below will help you better define the complex problems.

Approach for Complex problems:

• Explore to learn about the problem, as they require more creativity and innovative thinking skills
• Develop a theory and experiment to gather more knowledge
• Experimentation to discover patterns and gain more knowledge
• Repeat as necessary, with the goal of moving your problem into the another category
• Execute and evaluate, following the Plan, Do, Check, Act cycle

As the picture above indicates, Agile is the desired methodology to use as theory. Experimentation can be defined in predetermined amounts of time with predefined goals.

In one of my previous blog posts, I discussed using the Cynefin framework for providing guidance in determining the type of healthcare project you will be managing and the best SDLC methodology to use for that project.

The Cynefin framework defines four types of projects:

• Simple
• Complicated
• Complex
• Chaotic

Chaotic projects are the rare project, or not so rare, where the requirements are nebulous at best and execution of the project will consist of building “something” and gaining feedback on that something and continuing.

Think about a healthcare client that wants to implement a data lake because they know they have big data they want to capture in the future but really have no well-defined set of use cases for this endeavor. How would you start? First, you need to know you are in a chaotic project that does not have any real requirements. Then you need to determine the best methodology. The Cynefin framework tells us that Agile: Scrum would be the best choice.

Why? Because by taking advantage of the Agile Manifesto, we are uncovering better ways of developing software by doing it and helping others do it.

Through the Agile Manifesto, the focus is on:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

As the client tries to figure out what they really need, the principles of the Agile Manifesto fit nicely in a chaotic project:

• The highest priority is to satisfy the customer through early and continuous delivery of valuable software.
• Welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage.
• Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
• Business people and developers must work together daily throughout the project.
• Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.
• The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
• Working software is the primary measure of progress.
• Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely.
• Continuous attention to technical excellence and good design enhances agility.
• Simplicity, the art of maximizing the amount of work not done, is essential.
• The best architectures, requirements, and designs emerge from self-organizing teams.
• At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.

Using this approach will focus the customer on a single use case that can then be developed into “something” meaningful for a client.

Project managers working on large healthcare data warehousing projects have multiple methodology options at their disposal: agile, iterative, waterfall, hybrid. The challenge often becomes determining which methodology is best suited for the specific type of project.

This is where using the Cynefin framework can provide guidance for healthcare data projects. The Cynefin framework is used to help project managers, policy makers, and others reach decisions on how to execute based upon how well you know your end result. The framework consists of five decision making contexts of domains – simple, complicated, complex, chaotic and disorder – all of which provide guidance and direction for managers to identify how they will need to proceed with execution.

In “Simple” projects the requirements never change and the scope and execution paths are very well defined. Simple projects represent the “known knowns.” There are best practices and the relationship between cause and effect is clear. Change is not an option.

In “Complicated” projects the scope and requirements are well known however change is possible. The execution path is also well defined but flexibility may be required. The relationship between cause and effect requires analysis or expertise; there is a range of right answers. You need to assess the facts, analyze, and apply the appropriate good operating practice.

In “Complex” projects the scope and requirements are flexible and experimentation is required to determine what the end result needs to be. The execution also needs to be flexible as well to accommodate the changing requirements. In Complex projects the cause and effect can only be deduced in retrospect.

In “Chaotic” projects the scope and requirements are non-existent or ambiguous to the point of being non-existent. Execution will consist of build “something” and obtain feedback. In Chaotic projects cause and effect are unclear. Any action is the first and only way to respond appropriately.

The Cynefin framework quadrant you are in determines the proper project methodology to use to best implement your healthcare data project. The picture below highlights the suggested methodology:

In future blog posts, I will explore what and why each methodology is best suited for each type of project defined by the Cynefin framework.