Data

Default to truth

Why do we default to truth when someone is lying? Most people default to truth until their doubt and the facts tell them otherwise. 

My default belief has been set using the 80/20 rule. 80% of people tell the truth and do the right thing. Based on Talking with Strangers by Malcolm Gladwell, it’s a reasonable starting point.

However, I was in a meeting with healthcare executives when the physician founder challenged my belief. He believed only 20% of people do the right thing and that most will act in their own self interest. 

Work vs. Life

So which is it? It’s something that I’ve been rumbling with since that meeting. Let me start this discussion by sharing some additional input from colleagues that will help you understand why.

The Director of Health and Safety for a large employer organization believes 60% of physicians fudge the facts to turn medical claims into worker’s comp claims to get higher reimbursement [aka: payment].

A Finance Executive in private equity shared that he believes everyone seeking financing from them is lying. The only way they get to the truth is doing careful due diligence even if the company has been audited. They don’t put much faith in auditors either.

What I learned is that context matters. These people don’t believe everyone is lying all the time but in a professional context [and more specifically when money is involved] their default level of trust is lower. Whether it’s experience that has raised their level of doubt or use of data, they have learned to verify the facts rather than trust what people say.

Life

In the broader context of life, we don’t always have data readily available to tell us whether or not someone is lying or telling the truth. 

No one is good at spotting a liar. Looking someone in the eyes, reading their body language or talking to them doesn’t make you a good judge of truth. In fact, these attempts to discern truth blur whatever facts are available and consequently, rarely result in the right answer.

We have to believe people are generally good even if they tell a lie or two. Society wouldn’t function if we didn’t.

However, when we have doubt, we need to look to the data and trust the facts so that our feelings and biases don’t blur our judgement.

As with any Malcom Gladwell book, Talking with Strangers is brimming with great stories – and yes facts. 

#metoo

Talking with Strangers includes several stories about rape and why it’s hard for people to discern the facts in legal cases. The stories clarify the laws in each case, highlight the added issues if someone is intoxicated and discusses what constitutes consent.

Post #metoo everyone should have a clear understanding of these cases to help guide their behavior and to judge the facts. It’s worth your time to read the book.

Engineering vs. Design

What’s the difference? 

Both engineering and design start with a problem but the approach to solving the problem is different. 

Engineering is about distilling data and allowing the data to dictate the solution. The problem with an engineering only approach is that there is usually more than one way to solve the problem. Hence the need for design and design thinking.

Design thinking uses brainstorming to surface all the different ideas, rapid prototyping to test the more viable ideas and iteration to apply the lessons from the prototypes.

Design often gets stifled in the healthcare industry. From my experience there are two reasons:

1/ The situation has become dire and something needs to be done quickly.

2/ The executive team is committed to one management philosophy that leaves little room for creativity.

Getting painted into a corner is never good place to start. So how do you get out of it?

Data

Everyone needs quality data to make sounds decisions whether in a clinical or business role. When the data is bad, we end up wasting resources solving problems that don’t exist and overlooking the real issues.

Did you know that only 30% of the analytic results in healthcare organizations are accurate? 

It was one of the stats that I learned from Health Catalyst recently and based on my own experience seems about right.

Part of the issue is the old adage “Garbage In/Garbage Out” and the other part is a lack of consistency in defining and extracting the data elements. 

Good design makes data capture as painless as possible and helps to standardize the dataset to make the data meaningful, actionable and readily accessible to all users. 

Start with why

Lean is good but can be limiting without design thinking. There isn’t much difference between Just In Time inventory and Kanban. Yet when one fails, we try the other without giving enough thought to why.

Just in time inventory in healthcare has never worked all that well because it’s too complex for the endusers to maintain. Implementing Kanban with a client made me realize that the system wasn’t going to work much better if at all for the same reasons.

The user’s needs were never really considered in how the system was implemented and once the implementation was started there was no iteration to refine it. Sound familiar? 

Just in Time inventory and Kanban are good frameworks but there is no one size fits all solution. Design thinking is about considering all the issues underlying the problem, the stakeholders and the patients served to solve the problem.

How matters

How matters more than most leaders thought.

Corporate America is changing. Business leaders are realizing that they need to think beyond the bottom line.

Some investors are pushing back but what they might not realize yet is companies can do even better when they consider the social and environmental impact in their policies and business practices.

Haven

Haven Healthcare is the new healthcare company formed by Chase, Amazon and Berkshire Hathaway that is led by Atul Gwande MD. Dr. Gwande has been sharing his experiences, thoughts and insights about the cost and quality of healthcare in his books and articles for more than a decade. 

Since formation, the company has been working to understand the needs of their patient population so that they can “create new solutions and work to change systems, technologies, contracts, policy, and whatever else is in the way of better health care.”

The “whatever else” in this case likely refers to the way American Corporations have focused solely on the bottom line. It should come as no surprise to any of us that Jamie Dimon, CEO of Chase is one of the leaders championing this change. 

He is likely getting some good data and management insights to support his position. Hopefully we’ll learn more about that when the Forbes article is published next month. Until then, you might want to check out this book.

Dying for a Paycheck

We’re likely going to hear about some of the work published by Stanford Professor, Jeffrey Pfeffer.

In Dying for a Paycheck, he shares countless stories and stats about the management practices that “literally sicken and sometimes kill employees” and that also negatively impact productivity and the bottom line. Wellness programs can’t compensate for the fundamental issues that exists in many workplaces and unfortunately, are not bending the healthcare cost curve as expected.

Researchers in Denmark are reportedly using prescription drug data to draw correlations between prescription drug use and the effects of entrepreneurship, organizational change, compensation and more.

My guess is that Haven is using their medical data to investigate the policies and business practices of the operating companies and drawing similar types of insights. It could be game changing for Americans and the healthcare industry.

Times change, we need to change as well. 
~ Nelson Mendela

Changing how

A lot of this might seem like common sense, but without data it is harder to convince people change is necessary.

I was an online learning provider during the dot com boom/bust days. We helped clients enhance their operations while providing a path for a brighter future for their employees. How you ask?

Our training solution provided the much needed training to those responsible for the revenue cycle and financial management of healthcare organizations. Most had never received formal training on the systems or best practices which from a financial perspective is a recipe for disaster.

Staffing decisions are emotional but became so much easier with data about the time spent on course work, modules completed and assessment results – all stats we needed to report as a CPE provider.  

We enrolled everyone in their required training modules and gave them time on the job to complete the course work. Some just didn’t complete all of their modules and not surprisingly, they underperformed in those areas of their job. It was a clear indication that they had no interest in the work.

Rather than terminating their employment, it was my opportunity to start a conversation about the right career path for them. There are really only three career options: 

1/ Develop functional depth

2/ Transition to a cross functional role

3/ Retrain for something entirely new

Even though the organization had less than 100 people, we were able to offer all of these options within the organization and financially, we had some of the best years. 

Investments in fundamentals and people pay off in companies of all sizes.

Training investments help people perform better on the job and prepare for a brighter future. Many of the people who successful completed our courses have already transitioned into new jobs. They didn’t have to experience the stress of having their job eliminated as some are experiencing now.

Industry leaders need to be making these types of fundamental investments to be profitable and accountable to all constituents going forward. Those leading in a strong viking and victim culture such as in law, finance and tech might find it harder to make the mental shift but it is time for change.

Good Data

Only 30% of the analytic results in healthcare organizations are accurate. 

It was one of the facts that we learned on the Health Catalyst webinar this morning and based on my own experience seems about right.

Part of the issue is the old adage “Garbage In/Garbage Out” and the other part is lack of consistency in defining and pulling the data elements.

The new Health Catalyst population stratification module standardizing the datasets makes it easy enough for business people and maybe even some clinical people to pull their own data. It’s a huge plus especially for healthcare organizations conducting research.

Garbage in/Garbage out needs to be addressed with better user interface design, refined data capture requirements and compliance with medical record documentation. There is just no way around it.

Everyone needs quality data to make sounds decisions whether clinical or business. When the data is bad, we end up wasting resources solving problems that don’t exist and overlooking the real issues.

Improving Quality

I’m reading Stephen Pinney‘s book called How Hockey can Save the Healthcare System and highly recommend it. Why?

The section on Quality addresses one of the most important lessons for Administrators….don’t always trust your reports. Dr. Pinney highlights the problem in his example with the Pre-Operative Surgical Checklist.

According to the Administrator, the checklist was preformed consistently for two years without issue. The problem was that it wasn’t performed correctly. Significant errors resulted and were unreported.

One of the most important lessons that I have learned from working closely with Medical Directors is that they know the business. When they say something is wrong, something is likely wrong. Administrators need to dig into the details to get to the bottom of the issue rather than dismissing them.

Interestingly, Dr. Pinney and I have uncovered the same issue. Data is often missing and when data is missing – the reports are wrong. Administrators need to understand why the data is missing and take the steps needed to ensure it is consistently captured. It’s a matter of life and limb – literally.

According to Dr. Pinney, these types of quality and process improvements are key to systematizing medicine and achieving the third aim. However, it all starts with accurate data.