A Student's Take on A.I. in Healthcare

Hi fellow Clinical Informaticists, workflow designers, and other clinical architects,

Today's blog post is a slight deviation from my usual posts - It's actually a guest post, from a smart young college student, Paul Lestz, who I recently had the good fortune of working with on an educational internship.

Paul's particular interest is related to the use of Artificial Intelligence (A.I.), so we discussed the current state of A.I. in healthcare, and ways to implement this technology to a broader audience. 

So I'm very happy to report that, after reading Paul's blog post below, that 'The Kids Are Alright' - If this is what our future leadership looks like, then I have great confidence in our future. 

Please enjoy Paul's post below : 

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If after an exhaustive examination of data, an artificial intelligence (A.I.) algorithm were to recommend termination of care for a relative - how would you react? How would you feel if this type of recommendation or decision was made solely by an A.I. algorithm, with no clear human oversight? Does it help to differentiate a recommendation from a decision?

Currently, there are few industry-wide reasons to be concerned - at least so far. While some healthcare institutions have begun the deployment of A.I. systems, we are not yet dependent on them for these types of high-risk decisions. Human doctors still have responsibility and remain in control - which means now is a good time to educate ourselves on A.I., including its many compelling benefits, potential risks, and ways to mitigate those risks.
 
While reading, please remember - A.I. is a complicated topic, that warrants our attention. Turning a 'blind eye' to A.I. does not mean that the field will not continue to expand into every industry, including healthcare. I hope this post provides some helpful education - as a starting point for future discussions - and helps to reduce the initial intimidation that A.I. discussions often induce.

 

Why do I believe that A.I. will continue to expand into the healthcare industry? It's because of the many potential benefits of using A.I. to manage the high-risk scenarios that healthcare workers commonly encounter. Among others, here are some major benefits offered by A.I.:

 

Adapted from: Artificial Intelligence in Medicine | Machine Learning | IBM

• Cutting through the noise - A.I. can help make sense of the overwhelming amount of clinical data, medical literature, and population and utilization data to inform decisions.

• Providing contextual relevance - A.I. can help empower healthcare providers to see expansively by quickly interpreting billions of data points - both text and image data - to identify contextually relevant information for individual patients.

• Reducing errors related to human fatigue - Human error is costly and human fatigue can cause errors. A.I. algorithms don’t suffer from fatigue, distractions, or moods. They can process vast amounts of data with incredible speed and accuracy, all of the time.

• Identifying diseases more readily - A.I. systems can be used to quickly spot anomalies in medical images (e.g. CT scans and MRIs).

From my perspective as a student, these are all compelling examples of how A.I. could help develop healthcare into a more modern, efficient, and reliably data-driven patient-care system.

 

To do this, however, also requires an examination of the challenges that A.I. can bring with it - unsurprisingly, extremely new technology sometimes brings unexpected issues. Some of the known challenges of A.I. implementation include: 

 

Adapted from: The Dangers of A.I. in the Healthcare Industry [Report] (thomasnet.com)

• Distributional shift - A mismatch in data due to a change of environment or circumstance can result in erroneous predictions. For example, over time, disease patterns can change, leading to a disparity between training and operational data.

• Insensitivity to impact - A.I. doesn’t yet have the ability to take into account false negatives or false positives.

• Black box decision-making - With A.I., predictions are not open to inspection or interpretation. For example, a problem with training data could produce an inaccurate X-ray analysis that the A.I. system cannot factor in, and that clinicians cannot analyze.

• Unsafe failure mode - Unlike a human doctor, an A.I. system can diagnose patients without having confidence in its prediction, especially when working with insufficient information.

• Automation complacency - Clinicians may start to trust A.I. tools implicitly, assuming all predictions are correct and failing to cross-check or consider alternatives.

• Reinforcement of outmoded practice - A.I. can’t adapt when developments or changes in medical policy are implemented, as these systems are trained using historical data.

• Self-fulfilling prediction - An A.I. machine trained to detect a certain illness may lean toward the outcome it is designed to detect.

• Negative side effects - A.I. systems may suggest a treatment but fail to consider any potential unintended consequences.

• Reward hacking - Proxies for intended goals sometimes serve as 'rewards' for A.I., and these clever machines are able to find hacks or loopholes in order to receive unearned rewards, without actually fulfilling the intended goal.

• Unsafe exploration - In order to learn new strategies or get the outcome it is searching for, an A.I. system may start to test boundaries in an unsafe way.

• Unscalable oversight - Because A.I. systems are capable of carrying out countless jobs and activities, including multitasking, monitoring such a machine can be extremely challenging.

• Unrepresentative training data - A dataset lacking in sufficient demographic diversity may lead to unexpected, incorrect diagnoses from an A.I. system.

• Lack of understanding of human values and emotions - A.I. systems lack the complexity to both feel emotions (e.g. empathy) and understand intangible virtues (e.g. honor), which could lead to decisions that humans would consider immoral or inhumane.

• Lack of accountability for mistakes - Because A.I. systems cannot feel pain and have no ability to compensate monetarily or emotionally for their decisions, there is no way to hold them accountable for errors. Blame is therefore redirected onto the many people related to the incident, with no one person ever truly held liable. 

Rather than feel discouraged when comparing the benefits of A.I. versus these risks above, I'd like to share that there are solutions to many, if not all, of these known risks above - through commitment and detailed policy work.

 

For instance, let’s take a look at the challenge underlined above: automation complacency. At first glance, one might think it would be too difficult to resolve this extremely conceptual issue, intrinsic to the mind of the clinician. However, automation complacency serves little to no problem if the following workflow is implemented: 

 

(Sample policy/workflow for managing automation complacency - Click to enlarge)

 

I designed this visual to help simplify the complex process of reducing automation complacency to a few, easy-to-follow steps.

 

Resolving the issues related to A.I. does not mean instantly coming up with a single, lengthy procedure in the hopes that it will work. Instead, resolving challenges means breaking the problem down into pieces and isolating different steps in order to achieve the desired result.

 

When developing the flow chart above, I had to determine what exactly was the root of the unwanted issue: 

 

Q: How could a clinician be biased towards picking the A.I. algorithm’s result without considering alternatives? 

A: It would most likely be because they knew the A.I.’s prediction before/at the time they made their initial diagnosis. 

 

While we, as humans, might think that we are not biased by certain information, this assumption is often an illusion. Subconscious biases tend to be the most powerful because we do not realize how much they affect us.

 

In order to solve this problem, my workflow above mandates that the clinician provide and lock in their initial opinion before being provided the A.I. algorithm’s prediction. By doing so, we resolve our first issue of initial, subconscious biases.

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As I have just demonstrated, solving A.I.-related issues is often a matter of breaking down problems and coming up with small solutions that together, sum up to a working whole.

 

So, if there are often ways to mitigate the risks of these A.I.-related issues - are we good to go? The answer: it’s complicated.

 

Often, users (e.g. healthcare institutions) are not actually making their own algorithms. Instead, they purchase them. Therefore, one must consider various factors in deciding which A.I. algorithms to purchase. Unfortunately, after an extensive literature search, it doesn't appear that there has been a helpful, cohesive guide as to what factors to consider when purchasing A.I. solutions, so I would like to propose the following guidelines:

 

 

(Sample questions to consider in A.I. purchasing - Click to enlarge)

I created the infographic above to help frame some helpful questions to ask a vendor when considering the purchase of an A.I. solution.

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Generally, I hope that this piece helps to serve two primary purposes: 

1. The first is to convince you that, with good understanding and planning - A.I. typically brings about more good than harm in the world. 

2. (This second purpose assumes that you have already embraced the first) - The second purpose is to convince you not to take A.I. for granted, but to be thoughtful in the approach so that institutions (and the people who work at them) solve problems, purchase algorithms, and engage with the world of A.I. responsibly.

It's generally important to prepare and 'do your homework' before engaging in A.I. discussions. This preparation is especially important if we want to maximize the benefits of A.I. and minimize the risks. This post’s goal, therefore, is to bring the focal point of A.I. not to its use, but to its purchase. After all, a well-considered purchase combined with a thoughtful implementation often leads to more responsible ownership and successful outcomes. Alternatively, inadequate preparation can lead to unexpected outcomes. 

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As a student, and without a deeper knowledge of the exact workflow expectations for a particular circumstance, I am unfortunately unable to offer any more-detailed perspectives. However, I hope this initial post helps to 'get the ball rolling' on some important discussions related to proper A.I. planning, purchasing, and use. The right answers will still need to be evaluated and defined by planners, users, regulatory agencies, and society.

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Remember this blog is for educational and discussion purposes only - Your mileage may vary. Have any thoughts or feedback to share about A.I. in Healthcare? Feel free to leave in the comments section below!

Engineering Healthcare : Through A Historical Lens

 Hi fellow CMIOs, CNIOs, Clinical Informatics, and other HealthIT friends,

I'm writing today to share a presentation I recently did, on engineering Healthcare through a historical lens.

Seems like a peculiar title - but it summarizes a lot of the lessons I've learned in my roughly 13 years of both direct clinical and clinical informatics experience.

Below is my slide deck I used - Sharing it in case any of the slides help you develop your presentations on clinical Change/Project Management or Applied Clinical Informatics.

First - My intro slide : 

... which brings me to a brief discussion of our human history of documentation : 

It was pretty profound to me, when I first fully grasped the magnitude of this simple documentation loop, between both reading and writing information : 

Unfortunately, despite being open for business for over 300+ years - Healthcare has never really had an opportunity to really 'pause' to 'fix the plane' - so a lot of changes have happened serendipitously over this long timeframe : 

... which tells us a few things : 

So how can we do better? We need to start thinking like designers and engineers, and plan our workflows and changes by examining those documents that users interact with every day : 

... and if we look at those documents more closely, we see that roughly half of them are contained inside an electronic medical record - And the other half are outside. This gives us the roughly 24 building blocks of all clinical workflows : 

So if we depend on those 24 documents to be the building blocks of all clinical workflows - How do we help make sure these documents are as functional as they need to be? It all starts with functional definitions - Both what it's called, and what it does.

Once you have those functional definitions, this helps you create a working glossary and document templates, to help you quickly develop high-quality documents to build your workflows from : 

And to help you further develop your documents, it helps to understand how to build them in the most robust way - Aligning the concepts > terminology > templates > documents > workflows > goals/regulations > mission/vision : 

Now that you know how to engineer these documents for maximum benefit, it's helpful to figure out how to move (change) from Point A (current) to Point B (future). The distance between these two points gives you a rough estimate of which tools you will need to get there, and the project scope - How much time, people, and resources it will take to get there. 

Once you have your stakeholders and deliverables identified, it's helpful to orchestrate your change in a linear, organized, thoughtful, and predictable manner. For this, I offer up a helpful general-purpose change management recipe : 

If you don't have an organized process for managing/engineering changes - you could fall into one of these engineering pitfalls, which can lead to unexpected outcomes : 

... all of which should be aligned to your policies and procedures, the standards of your organization : 

A few final tips and closing thoughts, about planning, infrastructure, and clinical operations :

 

... and my final thank you and advice : "Control your documents, before they control you."

I hope these slides help you develop your own presentations on Applied Clinical Informatics, and the importance of solid clinical leadership and clinical change / project management. Thank you!

Remember - This blog is for educational and discussion purposes only - Your mileage may vary!

Have any secrets about policy writing, workflow development, or project/change management? Feel free to share in the comments section below!!

Optimizing your Intranet

Hi fellow CMIOs, CNIOs, Applied Clinical Informaticists, and other HealthIT friends,

It's been a while since my last post - As you know, healthcare is very busy adapting to changes brought about by our global COVID-19 pandemic. While the pandemic has and continues to be a great source of sadness and tragedy, it also brings a lot of change - I think a lot of this change is going to be very good, and facilitate lots of innovative, new ways to deliver care.

So for this post, I thought I'd piggyback onto my last post, "Welcome to Healthcare", by showing how helpful it can be to use a standardized index of healthcare to optimize your organizational Intranet. 

Why optimize your Intranet? It's the one 'filing cabinet' that everyone has access to, on their desktop, usually with one click. Imagine... Could your Intranet become a silo-busting, high-value tool that your employees use regularly to quickly find helpful information, that helps them troubleshoot problems, plan solutions, and easily learn about the people they work with? Could it also be an internal communication tool that invisibly teaches them about the structure of healthcare? I believe good indexing can do this, and I'll share why I believe this below.

But first - I'd like to provide some background, using one of my heroes, the brilliant Clinical Informatics pioneer Lawrence 'Larry' Weed, MD (1923 - 2017).

A treasured photo of me with the great Dr. Larry Weed, 

at the 2014 HIMSS Conference.

If you've ever written a SOAP note, it's because of Larry Weed's 1968 New England Journal of Medicine article, "Medical Records that Guide and Teach" - This was the breakthrough article that changed the way the whole globe writes clinical documentation. A copy of his original article in .PDF format is available on the Washington University web site by clicking here.

It's a fantastic read. What amazes me is that his SOAP note template allowed us, as clinicians, to organize our thoughts and then share them with other clinicians. One could argue that the whole specialization of healthcare in the 1960s and 1970s was made possible through his contributions to clinical documentation! 

In short - Larry Weed was right. You can't separate reading, writing and thinking - They are intrinsically connected. How you read and write shapes how you think. (By the way, if you'd like to learn more about him, you can also see his 1971 Grand Rounds at Emory University by clicking here.)

Now, borrowing from Dr. Weed's lessons that what we read and write shapes how we think - let's look back at the sample index we discussed in my last post. (Remember, your mileage may vary, depending on your institution's needs...)

Note : This [DRAFT] sample index may vary from institution to institution, depending on your needs. 

Also, for clarity and brevity, it also does not reflect the Board of Directors.

This general-purpose index can help us make seven very helpful Intranet homepages that guide and teach (thank you Dr. Weed!), with landing pages specific to each operational area of your institution, but yet connected to each other logically by links and strategically-designed news/announcement links. For example, using this index :

1. The Administrative Enterprise (1) Homepage would look something like this : 

Notice that in each of these pages, for institutional communication and awareness, there are three news banners for Administrative news local to this page, and also news from the other areas of the organization.

2. The Academic Enterprise (1.a) Homepage would look something like this : 

Here again, for awareness - there are three news banners, connecting Academic users with the events happening in Administrative/Research/Clinical Enterprises, and also the clinical services. 

3. The Research Enterprise (1.b) Homepage would look something like this : 

Again, with its three news banners, the Research Enterprise Homepage connects users with Administrative, Academic, and Clinical Enterprise news. 

4. The Clinical Enterprise (1.c) Homepage would look something like this : 

While the first level of news banners here is focused on Clinical Enterprise news, the second level connects with Hospital-Based, Ambulatory-Based, and Off-Campus Services, followed by a third with Administrative, Academic, and Research News. 

5. The Clinical Enterprise > Hospital-Based Services (1.c.i) Homepage would look something like this : 

Here, the primary news links are related to Hospital-Based News, followed by General Clinical Enterprise and Ambulatory Clinical Service News, followed by Administrative, Research, and Academic News. 

6. The Clinical Enterprise > Ambulatory-Based Services (1.c.ii) Homepage would look something like this : 

Here, the news links will help connect Ambulatory Users to Ambulatory News, followed by General Clinical Enterprise and Hospital-Based news, followed by Administrative, Research, and Academic news/announcements. 

7. Finally, the Clinical Enterprise > Off-Campus Services (1.c.iii) Homepage would look something like this : 

Here, the news links help connect Off-Campus Clinical Services with Off-Campus News, followed by General Clinical Enterprise news, followed by Administrative, Research, and Academic News links. 

Creating this sort of framework is not easy, and would require a significant investment in time and resources to implement and maintain this. One of the biggest challenges would be maintenance - How exactly would you maintain such a framework? Would there be one central 'webmaster' team, or would there be distributed 'webmasters' in different departments, each trained to maintain their area, links, news/announcements, and files?

That being said, I do believe there could be significant benefits to this sort of structure, by educating and empowering all of your employees to strategically find solutions within a few clicks of their landing page.

Either way - I hope this sample index and these designs help you think about how to strategically design and optimize your Intranet for your own institution.

Have any experience with Intranet optimization? See any areas for improvement? Feel free to leave them in the comments section below!

Remember, this blog is for educational purposes only - Your mileage may vary! Do not make any changes to your Intranet strategy without discussing, scoping, prioritizing, and approval from your own leadership teams!

Welcome to Healthcare!

Hi fellow CMIOs, CNIOs, Clinical Leaders, and any healthcare newcomers,

Today's post came after I recently had someone actually thank me (!) for quickly explaining the fundamentals of healthcare to them. 

After this conversation, it dawned on me that I've never really found a good welcome introduction to healthcare, this industry that I've worked in for years. It's been open for business, 24/7, for roughly 250+ years, but has never had a good opportunity to pause and ask itself : What we are doing, and how we are doing it? 

If you're a newcomer to healthcare, the welcome can sometimes seem a little cold and informal, something like this graphic :

While there are some reasons why seasoned healthcare professionals might greet newcomers this way, it doesn't actually help newcomers to understand healthcare. Sure, it's an industry that saves lives and treats diseases - but it can also make technology companies throw in the towel, and can frustrate politicians, providers, and patients alike. We could probably all benefit from newcomers having a good understanding of it's inner workings, before they get started.

So as a Clinical Informaticist, clinical translator, and general 'tour guide', I thought I'd write a friendlier, more explanatory piece, to help newcomers succeed by better understanding the fundamentals of this industry.

First, let's start with a sample diagram showing the overall structure of a typical healthcare organization :

*Note : This is a sample general-purpose structure - Many healthcare organizations will differ

 from this structure, based on their mission and other local legal, financial, operational, or regulatory needs.

*Also note : To help keep the chart simple, the Board of Directors is not depicted in the slide above. 

If I actually did a walking tour of this 'House of Healthcare' (not an org chart!), it might actually sound like this :

1. THE ADMINISTRATIVE ROOM

Walking into the administrative room, you can look around to see a lot of departments here, collectively tasked with running the organization and providing services to the areas below them. From here, some of the departments you can see include : Finance, Human Resources, Legal/Regulatory/Compliance, Privacy and Information Security, Contracting/Procurement, Employee Health, Facilities Management / Physical Plant, Public Safety/Security, Staff Education, the Switchboard/Operator, the Staff Directory, Public and Internal Communications, Enterprise IT/Informatics, Enterprise Project Management, Enterprise Analytics and Data Governance, and even the Library!

These departments are all busy providing the day-to-day support necessary for the Academic/Education, Research, and Clinical domains below them - And that means understanding both the common and unique needs of these three areas. (This is no small task!)

1.a. THE ACADEMIC / EDUCATIONAL ROOM

Walking down the path from the Administrative area, the Academic/Educational room often has a lot of schools/departments here, including Medical, Nursing, Dental, Pharmacy, and other Ancillary types of schooling. While these students and staff may also do research, and may provide clinical support (work) to the clinical enterprise, the main focus of this area is academics and education. So, for example, a Medical school might have several divisions : 

• Undergraduate Medical Education (UME)
• Graduate Medical Education (GME)
• Continuing Medical Education (CME) 

Before you think that these academic areas have it easy, keep in mind that clinical care and technology are constantly changing at an increasingly rapid pace. What was once considered desirable in the past - Memorizing textbooks full of science and clinical information - Is now considered passé, since a student who rotely memorizes facts is only memorizing clinical information that is rapidly outdated. Modern clinical educational thinking depends on not only learning a great deal of foundational knowledge, but also incorporating electronic databases and real-time decision-support tools into daily practices, with the goal of producing clinicians (doctors, nurses, and pharmacists) who continuously improve their knowledge while making decisions. 

Finally - Since the Research and Clinical Enterprises often depend on the students and staff from these Academic areas - They are a cornerstone of many healthcare institutions. (Except non-academic institutions, which do not have an academic/educational mission.)

1.b. THE RESEARCH ROOM

Walking up from the Academic/Educational room, you can walk down the hallway to the Research room, where you'll find a lot of very important departments, including : The Independent Review Board (IRB), Grant Management, Research Centers, Research Laboratories, Research Compliance, Research IT, Research Analytics and Translational science, and of course - a lot of highly-educated Researchers and Research Assistants!

This research is very important to us as a society, since it drives the foundations of medicine by creating the therapies and understanding that we all depend on. 

1.c. THE CLINICAL ENTERPRISE ROOM

Now walking from the Research Enterprise to the Clinical Enterprise, you'll notice some sudden, palpable cultural changes : 

• The Clinical Enterprise is largely open-for-business 24/7, so many of the staff are used to working in shifts and on holidays. 
• Patient safety is a constant focus of the workers here.
• A lot of people in these areas are wearing scrubs or white coats, and the air often smells faintly of antiseptic cleaning fluids.
• The fault tolerance is suddenly a lot less - requiring higher standards for hiring, budgeting, training, and implementing new tools. 
• Because it never gets to shut down for maintenance, and the low fault-tolerance - both the change management and project management are higher-caliber and noticeably different.
• The staff are often highly-educated, many with large amounts of student debt, so the salaries are suddenly higher. 
• The language and culture change, and may sometimes overlap or be different than the culture and language of the Academic/Educational or Research enterprises.. 
• Navigating the 'quasi-military' style clinical roles and responsibilities can sometimes be very complicated.

In this first top 1.c Clinical Enterprise box, we can see the many Clinical Enterprise Departments that support the patient care activities of all of the areas below them, including : Credentialing, Medical Staff Office, Nursing Department, Pharmacy & Therapeutics Department, Laboratory & Pathology Department, Diagnostic Radiology, Interventional Radiology, Non-Invasive Cardiology, Interventional Cardiology, Dietary/Nutrition, Physical Therapy, Occupational Therapy, Speech Therapy, Case Management / Social Work, Health Information Management, Registration, Access Management, Revenue (Billing/Coding), Housekeeping, Call Center, Scheduling, Clinical IT/Informatics, and Biomedical Engineering.

While many of these Departments above might be physically located inside the Hospital, it's important to note that the majority of these departments serve the needs of :

• the Hospital-based care areas, and...
• the Clinic-based care areas, and even ...
• the Nursing Home / Patient Home care areas.  

Let's now take a walk through the first of our patient care areas, the Hospital-based patient care locations...

1.c.i. THE HOSPITAL-BASED PATIENT CARE LOCATIONS

Walking through here, we can see a number of hospital-based departments / patient care areas in this room : 

• Emergency Department (technically an outpatient area!)
• Inpatient Unit - Med/Surg
• Inpatient Unit - Intermediate Unit (often Cardiac Telemetry)
• Inpatient Unit - Intensive Care Unit (ICU)
• Inpatient Unit - Labor and Delivery
• Inpatient Unit - Nursery
• Inpatient Unit - Pediatrics
• Inpatient Unit - Psychiatry 
• Perioperative Services (Pre-Op, OR, PACU) (technically all outpatient areas!)
• Ambulatory Procedural Suites (e.g. Endoscopy, Bronchoscopy, Interventional Cardiology, Interventional Radiology, sleep labs, EKG/Echos, etc.) (technically all outpatient areas!)
• Chemotherapy and Infusion Suites (note : in some organizations these are not hospital-based areas)

A lot of care is delivered in these hospital-based patient care areas! And keep in mind, it's a common mistake to either under- or over-estimate the acuity, complexity, or importance of these hospital-based areas - 

• The clinic-based areas can be every bit as acute, complex, and important!
• Many workflows start in the ambulatory clinic-based areas, and end in the Inpatient/ED (hospital)-based areas - And vice-versa! 

So understanding these many hospital-based patient care areas is only a part of the story.

1.c.ii. THE AMBULATORY (CLINIC) BASED LOCATIONS

In the Ambulatory (Clinic) based locations, you can find a lot of ambulatory clinics, along with sometimes some remote radiology services, blood draw services, and even some procedural and infusion services. For example, you'll commonly see Primary Care and clinics including : 

• Neonatology / Maternal Fetal Medicine
• General Pediatrics
• Family Medicine
• Medicine - General Internal Medicine
• Medicine - Geriatrics
• Medicine - Cardiology (General non-invasive and invasive/interventioal)
• Medicine - Endocrinology
• Medicine - Gastroenterology
• Medicine - Pulmonary / Sleep Medicine
• Medicine - Rheumatology
• Neurology - General
• Neurology - Movement Disorders
• Surgery - General
• Surgery - Neurosurgery
• Surgery - Ophthalmology
• Surgery - Plastics
• Surgery - Otolaryngology (Ear, Nose, & Throat, or ENT)
• Surgery - Orthopedics (Bone & Joint)
• OBGYN
• Maternal Fetal Medicine
• Psychiatry - General Adult
• Psychiatry - Pediatric and Adolescent
• Dermatology - General Dermatology
• Dermatology - Mohs Surgery
• Hematology and Oncology (often divides up into several specialty subdivisions of care)
• Radiation Oncology
• Genetics Counseling
• Physical Therapy
• Occupational Therapy
• Speech Therapy
• Diet/Nutrition
• Anesthesiology / Perioperative Medicine
• Urgent Care

... and more!

While they are generally only open during business hours, these ambulatory clinics provide a tremendous amount of care to a tremendous number of patients, and often have acuity, complexity, and safety issues on par with the hospital-based areas.

1.c.iii. THE OFF-SITE (NURSING HOME) or HOME CARE LOCATIONS

For our final stop in our tour of the 'House of Healthcare', we'll be stopping at the nursing-home and patient-home-based care. Yes, house visits still exist! These are growing areas for many healthcare institutions, and especially since COVID, this segment is only expected to grow in the near future. It often requires providers with unique documentation/billing practices, but this is an important source of care for hospice, homebound, and nursing home patients. 

SOME FINAL WORDS

Before we wrap up our walking tour, it's important to note that Population Health is a growing trend, which ties reimbursement strategies to improved health and improved patient outcomes. While much of the focus is on outpatient/ambulatory clinics, it can also impact a number of hospital-based workflows, and so it's important for everyone to understand the role that Population Health plays.

And for the particular segment that I work in (IT/Informatics), it's important to note that there are essentially four IT/Informatics domains that cover the spectrum of a typical healthcare organization : 

• Administrative (Enterprise) IT/Informatics (often includes Analytics/Data Governance, and infrastructure like servers, network architecture, security, interface management, hardware/software procurement, life cycle management, desktop/application management, etc.)
• Academic/Educational IT/Informatics
• Research IT/Informatics
• Clinical IT/Informatics

... each with their own unique language, culture, regulations, needs, and stakeholders.

I hope this has been a quick, helpful virtual tour of a typical healthcare organization - Remember, many organizations will vary slightly, based on mission and local financial, legal, or regulatory needs. If you have any questions or comments, please feel free to leave them in the comments section below!

Remember, this blog is for educational discussion only - Your mileage may vary. Have any insights into healthcare structures, or emerging trends that are shaping healthcare? Feel free to leave them in the comments section below!

Getting from A to B : Project Management for Clinical Leaders

 Hi fellow CMIOs, CNIOs, #HealthIT, and #Informatics leaders and friends,

Change is important. As a clinical leader, you'll want to know how to make workflow changes, either to help fix a workflow that's not ideal, update a workflow that needs updating, or build a new workflow. (As long as there are new journal articles and conferences, there will be necessary updates to clinical practice to stay current.)

So this week, I thought I'd write about a topic that can help a clinical leader to feel comfortable with making changes in their area: 

"How to get from Point A to Point B"

I once alluded to a problem with making changes back in 2016, when I blogged about the Red Sneaker Problem - And How To Fix It. To help avoid frustration for you and your team, it's helpful to understand 'How does anything change?'. Without understanding the change process, it can be hard to make change. 

Although clinical leaders often need to focus primarily on clinical services, functions, and expertise - it's still helpful to know the basics about two important things, related to 'how things get done' : 

1. Project Intake / Scoping - Helps you secure necessary people, time, and resources before you start a change project.
2. Project Management - Helps you effectively use those people, time, and resources to get things done (accomplish the change)

Without understanding these two steps, it can be very hard to accomplish much change. And without regular, smooth, and predictable changes, clinical leadership can seem more daunting than it needs to be. 

So as a brief introduction for new clinical leaders, let's review these two items in a little more detail. Borrowing some slides from a recent presentation I did for a group of clinical leaders, I present some high-level overview below. 

1. PROJECT INTAKE / SCOPING - 

Making change is work. It takes people, time, and resources, to move your CURRENT state (Point A) to your desired FUTURE state (Point B). 

Ideally, to make sure you have the 'gas' needed to drive your 'car' to where you want it to go, you'll first need to understand the scope ('size') of your project. Conceptually, think of this as collectively driving your car (with your team inside it!) from :

• Your CURRENT state (Point A)
• Your desired FUTURE state (Point B)

This is why I always advise people to formally map the current and future states. The distance between these two points is what will determine the scope (size) of your project,  and the work effort (and resources) needed to accomplish your goal.

• If you have the time, people, and resources necessary to get from Point A to Point B - Great! 
• If you don't... Then you may feel frustrated.

So to make sure you have a thorough, well-documented analysis that you can share with your project team - it's very helpful to formally document, in a folder, your CURRENT state, and also formally design your ideal FUTURE state, one that is formally signed off by the clinical leaders who oversee the clinical staff who will live in this new future-state workflow. 

People sometimes ask me : "Do I need to do this much for every change I want to make?" My advice : You only need to apply as much rigor as you need to get the change accomplished. E.g. : 

• For small changes (e.g. making some small changes to a documentation template) --> Usually, less rigor is required
• For large changes (e.g. implementing electronic med reconciliation at all transitions of care) --> Much more rigor is required

This exercise will not only help you scope your project, and identify the people, time, and resources you will need to secure - It will also help you formally plan a project, estimate the return on investment (ROI), and secure the necessary approvals before beginning your project. 

2. CLINICAL PROJECT MANAGEMENT

Once you have secured the necessary people, time, and resources, and have the approvals of your leadership to move forward - It's helpful to identify a formal, trained, and experienced project manager to plan, orchestrate, and lead your project. For a high-level overview, you can see the Wikipedia piece : https://en.wikipedia.org/wiki/Project_management 

For planning purposes, many experienced project managers might develop a Gantt Chart ( see https://en.wikipedia.org/wiki/Gantt_chart ), a sort of ordered series of steps, with time estimates and dependencies, that will be needed to finish the project and achieve the desired outcome. Similar, but also helpful is a Responsibility Assignment Matrix, sometimes called a RACI Chart. 

Experienced clinical leaders, especially those who have worked with good project managers, can often help a project by anticipating steps and helping to answer questions before they arise. While there are different types of project management (from the more traditional waterfall model, to newer agile methodologies), I've stripped down some bare essentials that are helpful to think about before starting any clinical update or improvement project : 

These are the ten steps (above) that I commonly plan and follow for clinical projects, where the rigors of step two (2) above are often necessary to help adequately scope and plan clinical projects, and help ensure that there are no unanticipated surprises later in the project. Note: Clinical Informatics professionals often work in steps 2, 4, 5, 6, and 9 above, working closely with end-users, analysts, educators, and project managers.

As a clinical leader, you will want to help champion change and updated practices. While there is much more to be said about project intake, scoping, planning, and execution, I hope this little introduction will help my friends in clinical leadership see the value of good project managers, and good project planning, and the role they play in getting things done.

Remember, this blog is for educational purposes only - Your mileage may vary. Always ask your local Project Management and Clinical Informatics professionals for guidance, and work closely with your clinical leadership to review, prioritize, and approve your projects before initiating any changes.

Have any stories to share about clinical leadership in supporting clinical projects? Have any tips or tricks to share from your own clinical project management experiences? Feel free to leave them in the comments section below!