“Learning is, by nature, curiosity.”
— Plato
The future of healthcare is at a crossroads and being shaped dramatically by a variety of significant trends. While the cost of healthcare continues to rise, the industry is facing a movement toward preventive and value-based care. At the same time, technology like wearable devices, IoT, and home services is empowering patients to be more engaged and more proactive with their own health. At the same time, the industry is struggling with the dilemma of data sharing and maintaining patient privacy and trust. These developments are affecting the role of caregivers and their relationships with their patients. One underlying constant driving toward the digital transformation in healthcare is the power of data and analytics.
Data is growing exponentially and is pervasive in every aspect of the healthcare ecosystem. Analytics is becoming more common to aid with delivering intelligence affecting our daily health, patience experience, and cost control. Technologies such as AI, 3D printing, and augmented reality (AR)/mixed reality (MR) are advancing medical research, preventive and predictive diagnosis, and ongoing care. In addition, more people are needed to support the needs of healthcare and processes are being evaluated to sustain the growth in healthcare.
In the previous chapters, we discussed a lot about technology, which is a key factor moving the healthcare industry forward. In-database processing delivers the promise of analyzing the data where it resides in the database and enterprise data warehouse. It is the process of moving the complex analytical calculations into the database engine, utilizing the resources of the database management system. Data preparation and analytics can be applied directly to the data throughout the data analytical lifecycle. Benefits include eliminating data duplication and movement, thus streamlining the decision process to gain efficiencies, reducing processing time from hours into minutes, and ultimately getting faster results through scalable, high-performance platforms. In-memory analytics is another innovative approach to tackle big data using an in-memory analytics engine to deliver super-fast responses to complicated analytical problems. In-memory analytics are ideal for data exploration and model development processes. Data is lifted into memory for analysis and is flushed when completed. Specific in-memory algorithms and applications are designed to be massively threaded to process high volumes of models on large data sets. Both of these technologies are complementary in nature and not every function can be enabled in-database or in-memory.
Hadoop is a technology to manage your traditional data sources as well as new types of data in the semistructured landscape. Hadoop is an open source technology to store and process massive volumes of data quickly in a distributed environment. Many misconceptions around Hadoop have created false expectations of the technology and its implementation. However, Hadoop offers a platform to support new data sources for data management and analytic processing.
Python, R, and Spark are popular and trendy analytical tools to analyze the abundance of health data. These tools are being embraced by data scientists and data engineers to obtain intelligence. Healthcare organizations are attracted to these tools for their lower cost investment and open source communities. By integrating in-database, in-memory Hadoop, and open source, it delivers a collaborative and harmonious data architecture for healthcare organizations to manage structured and unstructured data. From departmental to enterprise-wide usage, healthcare organizations are leveraging new technologies to manage the health data better, to innovate with analytics, and to create/maintain competitive advantage with intelligence.
If there is one thing that we highly suggest it is to review the use cases. Not only do they provide information that many of you can relate to but they also provide some best practices. These use cases are the ultimate proof that integrating data, people, process, and technology adds strategic value to organizations and provides intelligence in decision-making. Whether you are an executive, line manager, business analyst, developer/programmer, data scientist, or IT professional, these use cases can enlighten the way you do things and help you explore options.
We are barely scratching the surface when it comes to analyzing health data. The future of data management and analytics is pretty exciting. We are personally excited for the maturation of artificial intelligence and virtual/mixed reality. These two technology advancements are complex in nature but they also provide the most value. In addition, AI/AR/MR encompass machine learning, reasoning, natural language processing, speech and vision, human–computer interaction, dialog and narrative generation, and more. Many of these capabilities require specialized infrastructure that leverages high-performance computing, and specialized and particular resources with specific technical expertise. People, process, and technology must be developed in concert with hardware, software, and applications that are deployed to work together in support of your initiative.
A key question facing healthcare providers today is how to transform healthcare delivery while anticipating and meeting an emerging set of new expectations from patients. This will require significant change to the current model of care. The healthcare system in the United States has historically been focused on getting patients better once they have become sick. There are five major themes that are shaping the future of healthcare. They are:
An analytics strategy is needed in order to navigate the uncertainty these themes present successfully. Each theme touches data, people, process, and/or technology in one way or another. Data will be at the forefront. Data will be collected and integrated into data models. Data will come in all forms, from claims and billing data to electronic medical records to Internet of Things (IoT) to public use data. People will use process and technology to build analytical models tying data together that was once impossible, or at least ineffective and resource intensive. The ability to take streaming data and link it to claims data to electronic medical records (EMR) data to Centers for Disease Control (CDC) files to weather will allow for the potential of new intelligence to be delivered through deep learning algorithms. Data management, people, analytical capabilities, and a technology ecosystem will fuel insight-driven decision-making and algorithms and visualization techniques will allow for discovery and action that was once hidden.
Disruptive partnerships and market entrants will continue to challenge traditional business models, blur industry lines, and create new competitors and partners. Consolidations, both horizontally and vertically, will continue as organizations seek scale to help protect margins through efficiency and innovation. Cost pressures will drive flexible and innovative care delivery and require unprecedented modes of digital interaction. A deep understanding of population health and value-based care will become an essential factor for financial and competitive success. Patient engagement will be the crossroads of competition as all participants in the healthcare ecosystem seek to control it. An analytics strategy will help prioritize objectives and projects the stress that cost pressures and payment pressures of industry transformation will generate.
Medicare continues to create value-based programs to link performance of quality measures to payments. Their aim is to promote better care for individuals, better health for populations, and lower costs. Current programs include:
As Medicare continues to build programs, commercial and Medicaid programs are joining the fray. Taking longitudinal care of patients will be the responsibility of healthcare providers. The level of risk hospital providers will be responsible for will continue to increase. Out are the old fee-for-service models and in are the new innovative value-based care shared savings and quality-metric-based models. Your analytics strategy will need to generate models from primary care risk models to models identifying patients who are at risk for increased healthcare spend to future inpatient admission risk models for specific conditions that are considered “impactable” to identifying the services needed to keep patients in network to provide them with the best possible care to understanding why patients choose another network for some of the most critical procedures to leveraging genomic data. You can see the important role an analytics strategy will play.
You have probably heard Benjamin Franklin's quote, “In this world nothing can be said to be certain, except death and taxes.” Had Benjamin Franklin been alive today, he would have probably modified his quote, “In this world nothing can be said to be certain, except death, taxes, and rising healthcare costs.” According to the Centers for Medicare and Medicaid Services report, “National Health Expenditures Summary Including Share of GDP, CY 1960–2017,” healthcare costs have risen since 1960. With the healthcare industry under a cost-increasing microscope, the amount of investment and returns generated from an analytics strategy needs to be thoughtful and strategic, making sure capturing, integrating, and interpreting data from all available sources can be leveraged to deliver value in the form of dollar savings and quality improvements.
Your analytics strategy will help you uncover a deeper understanding of the health of populations you manage. Predictive algorithms can surface clinical opportunities for intervention and prevention for the populations you manage. Providing the right services at the right location will challenge operational and financial models to maximize throughput while providing services for the lowest cost while maximizing revenue and operating margins while providing the best clinical outcome. Not everything will be best solved by AI, but at least informed by AI and combined with human intelligence to create an optimal augmented-intelligence decision.
Healthcare is unlike any other industry. Providers sit in a unique relationship in patient's lives. Today, providers have a personal relationship with patients that should continue to become even more personal. Your analytics strategy will help rethink service strategies, capabilities, delivering potentially life-changing diagnoses, and culture that drive connections between Baby Boomers, Generation X, Millennials, Generation Z, and future generations. You will have to consider how social media, online communities, wearable devices, mobile connections, health literacy, and patient-generated health data will interact among all healthcare participants.
You can begin to understand the importance of your analytics strategy and the comprehensive complexity that it needs to address. As you develop your analytics strategy, developing internal business partnerships will be the key to your program's success. Partnerships will range from clinical to financial to operational. You should develop use cases to share success stories. Those success stories can cover one or more of the four parts of the analytics strategy from data, people, process, and/or technology. The use cases can explore the success in using data that was once siloed or did not even exist and now resides in a unified data warehouse that enables clinical (EMR and streaming data), financial (billing and claims data), operational (surgical schedules, scheduled visits, etc.), plus any other external data sources you are collecting, like census data, NOAA weather data, air quality data, etc. The use cases can affirm the importance of people and the importance of changing roles, capabilities, and investment required to become an analytically mature organization. The use cases can promote the development of teams around the principles of design thinking, lean, and agile. The use cases can establish the tools and processes for understanding the work, doing the work right, and doing the right work. The use cases should evaluate new technologies alongside your business partners to ensure the enterprise has the right tools to support an agile analytics way of working by enabling the design and evolution of architecture based on requirements.
Success is sustained responsibilities between the data and analytics team and the rest of the enterprise. The data and analytics team needs to provide data expertise around making analytics easier to consume. There is no need to show the complexity or the math. It needs to educate the consumer of analytics in the methodologies and how to interpret the results in context. It also needs to provide continuous monitoring that can allow for dynamic model recalibration, monitoring of model performance KPIs, communication around changes, or interventions that can impact results and explore new features. The rest of the enterprise needs to communicate feedback on interventions or process changes that can enhance models, use data-related insights daily, and embrace developing the required skills necessary for interpretation and decision-making.
Your strategy will allow you to benefit in the following ways:
The complexity of the healthcare problems we are being asked to solve cannot be delivered using traditional analytics. We are being asked to solve complex problems from demand modeling to capacity planning to population health management modeling to surgical suite planning, etc. The list continues to grow and the difficulty of the questions being asked continues to test the design complexities of model development. We are now picking from a list of techniques from Logistic Regression to Random Forest to K-Means and K-Nearest Neighbor Clustering methods to ARIMAX to Monte Carlo to Neural Network to Gradient Boost, by way of example. These techniques, coupled with the ability to visualize data differently, allows for insight that can unlock hidden opportunities that we were unable to identify before. It allows us to start changing the discussion. Is there opportunity where none may have been evident without using different analytical techniques or visualization tools? It is these opportunities to find insights that were hidden before the application of new visualization techniques that can give the healthcare industry the ability to lower costs while raising quality outcomes.
There is promise for AI in healthcare. It will give us better, more accurate predictive models. These models will be able to deliver meaningful clinical decision support functions, like problem list generation and differential diagnosis to operational throughput models to financial planning models. It will help with task automation like imaging analysis. But not everything is best solved with AI. Thoughtful augmented intelligence, the combination of human intelligence and artificial intelligence, will lead the way.
Finally, we would like to share key takeaways, views, findings, and opinions from along the journey. These takeaways, views, findings, or opinions do not have any particular order of importance; nor should they. It is a learning process with continued curiosity. They are also not independent of each other. Each takeaway, view, finding, or opinion has some overlap with the others in some shape or form. We hope that at the end of this chapter you will agree that taking the first step of the journey will be well worth the effort required and gain some value from lessons learned.
Be agile in everything you do, from projects to strategy. As Nike would say, if you have an idea, just do it. George S. Patton said, “A good plan executed now is better than a perfect plan executed next week.” Agile provides you the tools necessary to execute on a plan that will produce minimally viable projects instead of waiting until the perfect deliverable is produced. In fact, is there ever really a perfect deliverable? We believe not. Every project you will be delivering, from predictive analytical models to dashboards to a data warehouse, will require continuous monitoring and offer continuous improvement opportunities. Agile offers transparency in the work. All team members will have visibility as you track your project on a Kanban board. Sprint planning starts delivering feedback immediately and the team can start to show value instantly. The earlier you start delivering value, the easier it will be to manage expectations when project requirements change. Agile allows you to start to deliver working capabilities after every sprint, or at least progress that keeps the sponsor engaged. This invariably leads to higher satisfaction. Every project will not go as planned. There will always be roadblocks or changes in requirements, or at least the need for flexibility in changing requirements. Agile allows for the teams to control expectations and timelines and reduce the risk that the product deliverable will not meet the end user's needs. Agile methods maximize the opportunities for sponsor and end-user engagement by involving the sponsor and end user during the sprint planning cycle to product beta testing. It helps shape the “win-win” scenario for all involved.
Knowing your end user is a natural outcome as your analytic teams mature and complete projects. This will likely spur ideas from your team on potential improvements to end-user experience or tools to help end users do their daily work. Give your teams the freedom to “just do it.” An idea not worked on is just an idea. How will you know if the idea has any value added to the end user? There is only one way to find out, and that is to get started. This not only pertains to your end user. What about your daily work? We know everyone out there has ideas. What normally holds you back is time. Do you have time to work on your ideas with all of the other work you are responsible for? While your entire job can't be “just-do-its,” there should always be a balance between exploring new ideas and current workload. There is no magic number of hours per week we should allow for these types of initiatives, but leaders and team members should be able to discuss the potential value around ideas and find time to explore their viability. Remember, when you are sprint planning, you are in control of messaging and expectation management with your sponsors. You need to communicate the importance of bringing an idea into development to explore the potential benefits to the project. You do not want to stifle idea creation by not finding time to try new things. Not providing an avenue for team members to explore ideas they created will only diminish idea creation over the long haul. Attitude is everything and limiting the exploration of any team members will lead to a shutdown of ideas. They will have the mindset, “Why waste my time coming up with new ideas, when I can't work on them?” This is not to be confused with the lean principle we discussed earlier. Those just-do-its are quick-win, simple no-brainer solutions that have been identified and are in the backlog. These are known solutions just waiting to be resourced and can be grabbed during a sprint cycle when time frees up.
Check ego at the door. This requires lifelong learning – no one is too old to keep learning. Ego does not lead to collaboration. You have probably heard this phrase repeatedly. If you are the smartest person in the room, you are in the wrong room. When you are collaborating on your projects, who is in the room? Subject matter experts from many disciplines. Each expert brings smarts to the table. The collection of smarts allows the projects to move forward and produce successful outcomes. Checking your ego at the door allows you to objectively understand the strengths you bring to the team, the weakness you have, and your ability to convert that weakness by learning something new. It has been our experience that individual accolades stifle creativity and innovation through not actively listening. Don't jeopardize projects because you want to be singled out. Recognizing team wins creates a sense of accomplishment and participation from all individual team members. Success in this new era of advanced analytics and technology will require teams of subject matter experts. Don't allow egos to create an atmosphere of animosity. By checking egos at the door, you are already ahead without even realizing the benefits that will come throughout the project.
The only one holding you back is yourself. Healthcare is a complicated industry. Couple the rapidly changing technology landscape, advancements in artificial intelligence, and the rapidly changing healthcare landscape and you have a disaster waiting to happen if you are not committed to continuous learning. This doesn't come easy. There is a time commitment. That doesn't mean you need to learn for two to three hours daily. It means you need to make a commitment to learn every day. Maybe it is only 10 minutes, but make a goal. The best way to boost knowledge is through reading. Read every day. Maybe that goal is to read about something new every day related to the healthcare industry, or about a new technology or an artificial intelligence method. Maybe it is not healthcare-related, but business leadership–related, or learning from a different industry. Maybe that goal is to watch a video about AI or a new technology. There are other alternatives to reading, like audiobooks and podcasts. The point is, learning can be in any subject area. Take the time and effort to try and schedule that time to learn. If you do not make a habit of carving out the time to learn, that time will be filled with other activities, either work-related or personal activities. There is nothing more rewarding than learning something new. Continue to add to your knowledge base. Not only will you feel better, but your brain will thank you and you will be ready for any opportunity that comes.
Don't let technology drive the solution. Count on understanding the problem and requirements and designing a solution around solving the problem. AI is not a verb. You will be tempted to think the next bright shiny object, in this case, technology, will solve the problem. If it were that easy, everyone would have done it. Technology is part of the solution. As we have laid out in the book, driving to success requires a strategy around data, people, process, and technology. Understanding how technology will play a role in the deliverable of the solution involves gathering business requirements and functional requirements. It is making sure you can clearly define the objective(s). What business insights are you trying to deliver and how do you plan to show them? How often do they need new insights? What happens if the new insights are not delivered timely? What type of functionality do you need? Who will be using the outputs? Do you need different levels of details? It is important to interview the end users and document their answers to provide the teams with clear understanding and accountability of what needs to be delivered. It is at that time that the teams will be able to design the most appropriate solution with the right underlying technology to deliver a successful business value.
This may sound simple, almost a no-brainer. Projects often fail because we do not take the necessary time to understand the problem fully, or collect the necessary requirements and design a solution that works for the end user. Solutions are built by the analytics team without enough time spent with the end users. We have explored many different tools to help the teams make sure no stone is left unturned. The most important tool is curiosity. Most of you have heard of the five whys. Use it here by asking “Why?” five times; it really helps you understand every element in the requirements. This level of due diligence now will save time later. No one wants to stop a project because a requirement wasn't fully vetted. Spending the time at the beginning to collect details that will lead to full understanding of the problem, all the necessary requirements, and a minimally viable product will lead to an ultimate solution that is unambiguous to all teams.
Many times, we get caught up in the artificial intelligence hype cycle that AI will solve everything. Whether you go to conferences, talk about projects internally, or converse with your peers, AI seems to be at the forefront of every conversation. In fact, you see or hear comments like, “Just do some AI and see what happens.” AI should not be used without business judgment. Instead of just “AIing” a project, can we take advantage of data and AI techniques to disrupt business, clinically, operationally, and financially through thoughtful augmented intelligence to enhance decision support.
Collaboration and engagement is needed at every layer of the organization between sponsors and analytical teams. Mutual victories. Communicate success stories. Project success relies on collaboration and engagement at every level. Think about why most of your projects fail or get put on hold. I suspect the reason outside of funding the project is a lack of collaboration and engagement. Business requirements are naturally dynamic and leveraging real-time feedback from end users through their engagement keeps alignment around project objectives to potential timeline adjustments to challenges that need to be addressed across horizontally and vertically team dynamics. Without the necessary collaboration and engagement, projects just stall and lose focus and direction. Collaboration and engagement will encourage innovation and inspire fresh thinking. When has a brainstorming session been unproductive? That is not to say every brainstorming session will be considered a success. Some sessions will generate volumes of ideas; some may not. People never really turn off their brains after a session and a seemingly unproductive previous session may wind up very productive in the next session. Brainstorming sessions will create a melting pot of ideas that the team owns. It is this ownership of ideas by the team that generates new ideas. The inescapable conclusion, collaboration, and engagement breeds project excitement through accountability, ownership, and learning. This ultimately continues to drive projects forward toward the first minimally viable product. Once you reach that milestone, the only barrier is believing you can let up on collaboration and engagement efforts.
No matter how the project originates, whether you have a sponsor, it supports organizational goals or objectives, or your analytics team sees value in exploring a project and then taking it to potential end users, all projects should have mutual victories. Why wouldn't they? This theme of the book is about a strategy that is applied to enable shared success from providing insights to the decision-making process to leveraging work across multiple projects to learnings from all projects, whether they were successfully put on hold indefinitely or just dissolved when value could not be realized. Building models is easy; answering the right questions to achieve mutual victories is hard.
Success is not just a local event and shared with only the teams involved. Find ways to communicate success stories to the organization. This will draw people in. Who doesn't want to be part of a success story that is shared? Whether it is through the internal intranet, monthly or quarterly analytics meetings, or corporate email communications, find a way to share the success stories. Not only does sharing create goodwill, but it creates excitement and interest. Interest will draw additional use cases and, as interest continues to grow, you create a self-sustaining backlog of projects. Isn't that what you want?
Analytics is like taking a class in reverse. You are asked to take the exam without knowing the content and then you take the class. This is best explained by an example. Let's assume you built a forecast. What is the purpose of a forecast? To provide an estimate of what the future will look like. Let's assume you produce a forecast for the next eight weeks. This is the exam. Now, for the next eight weeks, the “content” is unveiled. That is, over the next eight weeks the actual results will come in and you will see how your forecast performed. You see, it's like taking the test first. You do not get the luxury of knowing the actuals or understanding the content until after the forecast period is over. It will be like that for every model you build. Sure, you have training sets, but the actual exam is what is important. This can represent a challenge in gaining confidence in using and interpreting the results. It is just like playing a musical instrument. The more you practice, the better musician you become. Treat analytics the same way. The more you use analytics, the more confident you will be in consuming the results and applying them in your daily decision-making process.
There will be hard times and frustrations, don't let that consume you. Put setbacks behind you. Celebrate every win, no matter how small or insignificant you think it is. Take time to reflect – learn from your wins, but learn more from your mistakes. This is probably one of the hardest lessons to learn. When you have setbacks, it is how you respond and it starts with attitude. You will definitely be disappointed and perhaps even angry. That is normal behavior but you have to move on. Think of a football player or baseball player who just lost a game, a Super Bowl or World Series. What do you hear from them? You hear disappointment and anger. What do you also hear? They put it behind them. When we are talking about a game during the regular season, they need to put it behind them within 24 hours. If they do not? There is a good chance it will affect their performance on the field during the next regular season game. What if it is the Super Bowl or World Series? Sure, it may take longer, maybe weeks or until the next training camp starts, and they eventually put it behind them. The one consistency is they use the time on how long they will focus on feeling disappointed or angry. The same holds true here. There will be times during your career where there will be a setback. You will have a range of emotions. The same principle holds as for a sports athlete. You need time to express your feelings, and then you need to refocus your energy. There will be learnings to be had. You need to understand how you contributed and what you can do next time to circumvent the same outcome. Acceptance allows you to move on and ask, “What's next?”
Think about what you are embarking on. Four seemingly separate parallel journeys (data, people, process, and technology) are connected in driving toward one goal. That goal is to execute on your analytics strategy to create a competitive advantage. Think about any project that you will be starting up or the analytics strategy you are executing against. Your analytics strategy is not going to be completed overnight, nor will it be completed in a week or month. We are talking year(s) potentially to deliver on all parts of your analytics strategy. Projects will act much like your analytics strategy. These projects will not be completed overnight, and probably not be completed within the next week. Projects will have project plans with backlogs, tasks, and milestones with a targeted delivery of a minimally viable product that will lead to a product with full functioning capabilities at some point in the future. As we learned earlier in the book, there are plenty of different tools, like lean principles and agile techniques to help you navigate to the finish line. As tasks are starting to be completed, you move them through the Kanban board. Do you really take the time to celebrate? Should you really celebrate all of those little tasks that lead to significant stages of the project or strategy? The simple answer: yes. Why? A few reasons come to mind – attitude, importance of the current moment, and appreciation. The attitude of the team is a collection of the individual attitudes of its members as well as the leaders and sponsors. Positive attitude is contagious. When one person succeeds, everyone feels better. Creating an atmosphere of positive attitude will help overcome the most difficult challenge. Understanding the importance of the current moment can give the team confidence it is on the right path. Every task completed inches you closer to completion. Quitting is the only way a project fails, so make sure the current moment is celebrated and appreciated. Every step you take, no matter the size, is a step forward. You cannot change the past, so making sure you understand the current moment will make for a brighter future. Appreciation is important in two respects. First is around people and second around the work. Appreciation does not mean bringing in coffee and bagels or throwing a pizza luncheon for the team for every little task completed. Appreciation is showing every person on the team respect. There will always be a chance for a pizza luncheon after a milestone. Respect and acknowledgment of work well done creates positive influence, energy, and outlooks. Appreciation is a way to ignite a person's inner passion to do a job well. Appreciation is the celebration of excellence in one's work. The work is going to be hard. Acknowledging that fact is important and teams will appreciate that. There are going to be times when the team is challenged to find a solution, maybe even change course, or does not have an answer. Appreciation means the team has the power and confidence to say, “We don't know” or “We don't have the answer.” One thing is clear, we are certain that will lead to answers. Appreciate the fact that not everyone has all of the answers. What the team is really saying is, “We don't have the answer – yet.” The team will find a way. Never take for granted people and the understanding the work is hard.
In today's business climate, where analytical productivity is measured in throughput of projects, algorithms, or dashboards that are placed into production environments, we normally fail to account for reflection. There will be projects that succeed, others that fail, and some that are just put on hold never to be resurrected. There are so many lessons that can be learned if we take the time to do a postmortem. Part of every project should be a discussion and reflection. We should focus on questions like: What worked well? What didn't work so well? What should we continue to do? What would we do differently? Did we meet timelines? How were our time estimates for completed tasks? What recommendations would we make to other teams? Collecting great feedback is time misused unless you disseminate the information, whether it is written or verbal.
Be creative – don't be afraid to try something new. Explore everything. Have accountability. We tend to lack creativity for a couple of reasons. First, it may take more time than the end user may be willing to wait for their minimally viable product. Second, with creativity comes failure. Not every creative solution will work and failures can be seen as time and resources wasted. Third is the old adage of, “This is how we have always done it in the past.” Fourth, leaders might not be comfortable with creative failure. Creative setbacks can lead to missing milestone timelines. Don't let any of these reasons stop creativity. There will be times when taking chances on creative ideas is wide of the mark and doesn't produce the desire result. Make sure you can clearly articulate the benefits you expected to receive and what experience was gained. Every project you work on does not come with a step-by-step instruction book. Granting creative licensure to the team will motivate them to build the best solution possible. It is invoking critical thinking skills that can be leveraged in future projects. Creativity keeps the team fresh and interested. Creativity will lead to more successful outcomes. Competitive advantages do not come from doing the same thing over and over again. Competitive advantages will be stimulated by creativity. Big breakthroughs come on the heels of creativity, not doing the same old routine. In the end, creativity will bring value to the end user. You just need to make them see that failures were lessons applied in disguise.
Accountability should lead to high performance, not punitive actions. When you think of accountability, it is normally around situations where something went wrong and people want to know who was responsible. Or when someone is going to hold you accountable, it leads to fear. Accountability should be a positive experience and starts from within. Accountability is making commitments and being helped to them. Commitments should be specific. Some examples are: I will do some task by some date. I will follow up with you by some date. I will provide you feedback by this date. As you can see, we are not using indecisive words like “try,” “should,” or “might.” We are also making accountability at the individual level. There is no team accountability. When you think of your Kanban board, tasks are assigned to an individual. Therefore, accountability at the individual level follows. Accountability starts with those individuals not afraid to lead the effort. A great resource is Winning with Accountability by Henry J. Evans for understanding how you can create a culture of accountability in your organization.
Trust your strategy. Success is not perfection. You will exhaust an immense amount of time building an appropriate strategy, whether it is around projects or creating your analytics strategy roadmap. When you have gotten that far, then you are on your road to success. There is no such thing as a perfect strategy. There are always going to be roadblocks, challenges, and difficult times. You had the right skills to get you that far. Don't look around thinking someone else is challenging your strategy. Trust in yourself. Trust in your skills that got you this far. Not using your skills will lead to doubt. Doubt will lead to uncertainty. Uncertainty will lead to fear. Now, you have skepticism creeping into strategy execution. Your strategy will have weaknesses. Trust that your team will find ways around those weaknesses. Your strategy will require help. Asking for help instills trust in others. Speaking up shows humility, not lack of intelligence. People will see this as you are not the smartest person in the room and can foster learning, problem-solving, and innovation, exactly the outcome you are looking for. Ultimately, trusting your strategy comes down to one simple point. You built the strategy because you thought it was best for your organization. What better reason do you need?
We have all heard some variation on “Don't let perfect get in the way of good.” Striving for perfection on a project just leads to delays, frustration, and disengagement. Why? What is the definition of perfection? Everyone has a different interpretation. We discussed the concept of a minimally viable product (MVP). Are these MVPs perfection? Of course not. It is an agreed-upon product that everyone agrees can be used. Is that the measure of success? They are the products that will continue to be enhanced over time. Capabilities will be tested. Some of the capabilities will be accepted, and some will not. There will always be new ideas generated over time. Striving for perfection leads to paralysis, that is, a project never gets off the ground because there is a fear that we are missing something. There is always another “what-if” we are evaluating. Success comes from being able to use the first MVP as intended. Continued success comes from enhancements and releases based on ideas generated over time. Perfection is a myth that needs to be discouraged in project-based work. It is subjective and everyone's “perfect” outcome cannot exist simultaneously.
Don't be intimidated by titles. Always challenge ideas and thought leaders in your organization. Always put your ideas out there. Have you ever sat in a meeting with an idea, thought, or input and never spoke up because of who is in the room? Do not let titles intimidate you from sharing ideas to thoughts to comments to concerns to recommendations. We cannot begin to list all of the characteristics that make great leaders. But a few of the characteristics that apply here are focus, passion, respect, confidence, caring, shared visions, and engagement. You will probably see some or all of these characteristics as they define a leader's style. What other characteristics can you think of that define their style? Would any of these cause you consternation to the point that you would feel uncomfortable or even silly to share your thoughts? Why are you working for the company? Why is your leader? Don't you believe you share the same drive toward your company's mission and values?
Every case study we examined in this book, the many projects that are ongoing, and the many projects that live in your backlog or have not been identified will benefit from challenging ideas and thought leaders. How else do you guarantee the best product? The best ideas are the ones that spur conversation and debate. Many times, without even realizing it, these conversations and debates take the form of strengths, weaknesses, opportunities, and threats (SWOT) analyses. When you challenge ideas, you should always consider a SWOT analysis or similar methodology as a tool to help you document your challenges. Whatever the final outcome on challenging ideas, this analysis will help you communicate to the necessary sponsors how you came to your decision. What is one thing that thought leaders of your organization excel at? Informed opinions. These informed opinions live in your strategies or projects. As a good steward to your organization, it is essential to challenge your thought leaders. We cannot think of a successful thought leader whose purpose is to just have their employees execute as automatons. Thought leaders always offer an unusual or unique perspective that should generate questions or thoughts.
Your analytics strategy will create opportunities that once may not have been possible. Maybe you didn't have the right data, the computational power, or technology to execute previous projects? Maybe your projects were always on a wish list that never could be activated? Ideas should always be considered, whether they are projects or parts of projects. Make sure you surface every idea. Being able to make sure every idea is heard gives the project teams every opportunity to explore value adds. Isn't it better to debate, discuss, and challenge every idea? The only bad idea is the idea not communicated. The only bad idea is an idea not vetted. The only bad idea is one that cannot be considered. Be mindful of your project's size and scope. That doesn't mean you should not put your ideas out there; it means managing expectations, deliverables, and release schedules. The project direction could be a straight path or it could pivot, based on changing requirements.
Don't be afraid to ask what the value add is on every project. Intellectual return on investment has value also. Not every project is going to have a calculatable return on investment (ROI). Check that, calculating ROI on some projects will be very difficult, could require years to see, or you may not have the necessary data to do the calculation. Regardless, you would be able to define clearly what the value add is on every project. If there is no value added, a more important questions needs to be considered: “Should we even move the project forward?” Some projects will have immediate ROI returns that you can calculate. One of the easiest ROIs to consider at the beginning of your journey is efficiency. Even with implementing new advanced analytics in your projects, many of the new technologies will offer efficiencies and gains in freeing up resources due to enhanced data gathering and computation techniques. Some of the ROI is based on adoption of the strategy. It is people coming to the platform and data and then leveraging data, people, process, and technology to create the value.
We discussed in Chapter 2 that people were the most important asset in an organization. It is the human element that adds intangible value to every organization. It can be hard to quantify intellectual ROI because the components are abilities like business knowledge, idea generation, innovation, and creativity. Intellectual ROI can also be things like algorithms and dashboards. Employee retention needs to be a key strategy every organization considers. When you find those “high performers” or “diamonds in the rough,” the cost of replacing those employees is not only in trying to find suitable replacements, it is the intellectual property that leaves the organization. Investing in people leads to consistency in your analytics team output. The last thing you want is to lose key intellectual resources to competitors.
We hope you have enjoyed reading about transforming healthcare as much as we did sharing our thoughts and insights. The quest for healthy intelligence is accomplished through a comprehensive strategy around data, people, process, and technology and cannot be accomplished by technology alone. Putting together a comprehensive strategy requires plenty of hard work, collaboration, and support across your organization, both horizontally and vertically. Find key business partners that share your vision and want to be part of your journey. Develop a framework of key use cases that will help communicate success to the organization along the journey. The key use cases should highlight building the components of the strategy to finding engaged business partners to delivering successful project outcomes. The journey will be long, difficult, and challenging and, at the same time, rewarding. Building consensus and support around your strategy will require support from key business partners that share your vision. Find projects that will show value. There will be times when the path in front of you appears to be blocked. Don't let those times discourage you. The quest for healthy intelligence starts with the first step. That first step is deciding to build an analytics strategy for the future. Now that you have decided to move forward, continue to put one foot in front of the other. Each step you take doesn't need to be a big step. Find those business partners that want to share in the excitement that is generated around being proactive instead of reactive. Good luck, and have a healthy journey!