EHR v Covid-19. Leading Indicators and COVID-19 Hospitalizations by Region (Guest Blog: Brendan Drew, UCHealth data scientist)

The Covid-19 pandemic is still quite uncontrolled in the US.

In this post, we’re going to walk through an analysis that was conducted by the UCHealth data science team looking at “leading indicators” that could help us to plan for a coming spike in COVID-19 inpatient hospitalizations before we actually see an influx of bed demand.

Perhaps, if we start to see more patients reporting a cough, fever, chills, and other flu symptoms, we would expect that this may indicate a growing spread of COVID-19. However, can we actually use the prevalence of these symptoms to predict how many ICU beds will be needed for COVID-19? What about less common symptoms of COVID-19, such as loss of smell or taste, that have been shown to be more predictive of COVID-19 infection?

While this may sound like a relatively straightforward question, there are a number of confounding effects that make it difficult. The above graphic shows the number of patients making an outpatient or virtual office visit due to a fever. As expected, there is a general downward trend as the seasonal influenza season subsides. However, there also appears to be a “spike” in reports of fever in early March in our Northern Colorado geography (orange line). Could this spike be quantified for future predictions?

Defining a “symptom” in our Epic electronic health system is complex. For example, symptoms can be documented as the “reason for visit”, but a medical assistant may or may not choose to report all symptoms as the visit reason. Besides “reason for visit”, our Epic team has developed a COVID-19 symptoms checklist that screens patients at check-in (completed by front desk staff). This list was expanded substantially in the midst of the epidemic based on new evidence (for example, loss of smell). The consequence is that we saw an increase in reporting of these symptoms in April, due to the new data fields, while our actual number of COVID-19 inpatient cases was declining. In short, there is a significant amount of noise to parse through before arriving at a prediction we can trust.

How did we go about identifying the signal from the noise? Knowing that there was no “right” answer, we tested different approaches. I’m going to focus here on the most recent modeling attempt that we have found to be most insightful. We started with the premise that the correlations between our independent variables (reported reason for visit, reported COVID-19 symptoms, and documentation of ICD-10 billing codes indicative of confirmed or potential COVID-19 infection) and our dependent variable (number of COVID-19 inpatient hospitalizations) would change over time due to trends in seasonal influenza and introduction of new codes/data elements in our EMR system. We therefore constructed separate linear regression models for the months of March (when the epidemic hit and we did not yet have IT system capabilities for tracking many symptoms), April (when COVID-19 cases hit their peak and then declined, accompanying a ramp-up in new IT system capabilities), and May (something of a “steady state” when seasonal influenza had passed and no major IT updates were made regarding COVID-19 symptoms or billing codes).

We wanted to test a large number of independent variables, and therefore chose to use a linear regression method known as LASSO regression instead of the traditional OLS modeling technique. LASSO regression introduces a regularization parameter that penalizes large coefficients in the model. Instead of optimizing to minimize prediction error, the model minimizes the below cost function:

  • Y: Dependent variable
  • X: Independent variable
  • β: Regression coefficient
  • λ: Regularization parameter
  • n: Number of observations
  • p: Number of independent variables in the model

In plain English: we reduced the complexity of the model and thus reduced the chance of spurious correlation or the influence of random “noise” in the data.

Our independent variables were reported outpatient symptoms and diagnoses in the seven days prior to the index date, and our dependent variable was the number of COVID-19 hospitalizations in the seven days after the index date. For example, on May 1 we fit the numbers of reported symptoms and documented ICD-10 codes from the prior 7 days (4/24-4/30) to the number of hospitalizations in the next 7 days (5/1 – 5/7). An astute reader will note that our modeling approach violates one of the tenets of linear regression modeling in that the observations are not mutually independent, but rather a time series. To mitigate this issue, as well as the small number of observations in a given month, we used a procedure drawing bootstrapped samples from each month 100 times, and for each sample, using a 5-fold cross validation process to determine the optimal regularization parameter, fit a LASSO regression model. A bootstrap sample is a random sample of the same size as your original data drawn at random with replacement from the original data, so in some samples data points for 5/1, 5/2, and 5/3 will all be included, some may only include 5/1, and some may include none of those data points.

Once again giving a simple English translation for those less interested in the modeling approach: we introduced some randomness to our data to give ourselves better confidence in our estimates of the linear correlation between each variable and our outcome of number of future COVID-19 hospitalizations.

The below table summarizes, by month, the average correlation coefficient from all of the LASSO regression models fit to bootstrapped samples of data from that month, sorted in decreasing order by the value in May. Please interpret the nomenclature as follows:

  • reason_visit: Indicates the variable is the reported reason for visit in an outpatient or virtual encounter
  • symptom: Indicates the variable is one of the COVID-19 symptoms selected from a checklist by clinicians at the beginning of outpatient/virtual encounters
  • icd: Indicates the variable is documentation of an ICD-10 code referencing confirmed or suspected cases of COVID-19
Variable NameMarch CoefficientApril CoefficientMay Coefficient
reason_visit_COUGH-9.809771.9958827.786421
reason_visit_FEVER-0.738250.4876012.66054
reason_visit_CORONAVIRUS CONCERN1.167884-0.160122.399324
symptom_Fever-0.527060.5537180.626149
reason_visit_SHORTNESS OF BREATH0.71668500.599447
icd_B34.23.0065470.2977230.311514
symptom_Vomiting0-1E-150.22527
symptom_Diarrhea000.179083
symptom_Shortness of breath0.241053-0.013260.134918
symptom_Cough2.255370.1000870.042621
icd_R68.89-0.427230.7904270.020276
icd_Z20.8280.254416-0.101330.002899
symptom_Red eye000
symptom_Loss of smell000
symptom_Rash000
symptom_Joint pain000
symptom_Sore throat000
symptom_Bruising or bleeding000
symptom_Weakness000
symptom_Abdominal pain000
symptom_Loss of taste000
symptom_Muscle pain00-0.10438
symptom_Chills00-0.15124
symptom_Severe headache0-0.53023-0.16017
icd_U07.10.253596-3.47782-0.24094

The strongest positive correlation with future COVID-19 hospitalizations in the month of May was “cough” as the reason for visit. At first, the trend in this correlation over time seems counterintuitive. Why would we see such a strong negative correlation in the month of March but a strong positive correlation in the month of May? Well, a reasonable hypothesis has to do with the ramp-up in COVID-19 testing coinciding with the end of the 2019-2020 seasonal flu. In March, we saw an overall decline in patients seeking outpatient care for a cough, likely due to both the end of seasonal flu and social distancing keeping patients from seeking treatment at medical facilities, while we simultaneously initiated widespread COVID-19 testing at our inpatient facilities and saw a rapid rise in confirmed cases. In May, by comparison, there was no noise from the seasonal flu influenza and no significant backlog in testing to ramp up.

We can also look at the distribution of the regression coefficient for the cough variable in our bootstrapped samples to better establish our confidence in the value. The below histogram shows the distribution of the coefficient across all 100 bootstrapped samples for the months of March (blue), April (orange), and May (green). Notice that for a large number of samples from March and April, the coefficient is near 0, while for the month of May it ranges consistently between 5-10. What does this mean? It means that a few data points in March and April are likely having a disproportional impact on the estimate of the linear correlation, while the correlation in May is more consistent regardless of which dates are sampled.

Examining the scatterplot for the month of May, we see that this linear correlation does appear quite consistent across the time period.

After all of this analysis, what are our big takeaways? Can we take our regression model for the month of May and start using it to predict bed demand? Unfortunately, this would be unwise. One month of data is too limited a timeframe for us to be confident in our model. While we see a significant correlation between patients seeking treatment for a cough and inpatient COVID-19 hospitalizations in the month of May, both variables declined over the majority of the timeframe. We would feel significantly more confident in our model if we observed a spike in inpatient hospitalizations preceded by a large number of patients reporting in outpatient settings with a cough, as opposed to the continuous decline. Hopefully, this never happens, but we believe a second wave of COVID-19 infections is very probable by at least next Fall or Winter. Our plan is to continue to update our model with new data, potentially including new data sources such as patient engagement with our Patient Line call center resources or Livi chatbot feature, through the next wave of infections and observe performance before deploying to assist in the management of hospital resources.

–Brendan Drew, UCHealth data scientist

Dialing in to an Aging Parents Telehealth Visit… Why aren’t more of us Doing it? (Guest Blog: Glenn Sommerfeld)

I forgot about my father’s memory and neurology clinic visit even though I had promised to go down to Denver with both of my parents to help them navigate the complex world of healthcare four months before.  A lot changed in those four months, most notably COVID-19 swept across the world and made its way into the US.  The pandemic placed my aging parents at a greater risk if they contracted the virus while traveling from Fraser, Colorado to Denver and my work schedule was beyond capacity as I added Federal and State COVID-19 reporting coordination to an already full project portfolio.  How could a take a day and a half off work?  How could my parents stay safe?

Telehealth and Rural (Mountain) Living

I decided to move on from my first health care job in neurophysiological monitoring to acute care in 2011.  I also wanted to move to the mountains of Colorado.  My parents already moved from Colorado Springs to Fraser, just outside of Winter Park, Colorado.  Yampa Valley Medical Center brought me on as a quality analyst before they were part of the UCHealth system.  After moving to Steamboat, I realized how remote and isolated Steamboat Springs, Colorado was from Denver and the other “Front Range” cities in Colorado.  Here are some fun facts about driving from Steamboat for medical care:

  • Steamboat Springs to University of Colorado Hospital and the Anschutz Campus
    • 169 miles
    • 3 Hours and 10 minutes if traffic is good
    • One major mountain pass (or two if Eisenhower Tunnel is closed)
  • Steamboat to Poudre Valley Hospital
    • 159 Miles
    • 3 Hours and 21 minutes if traffic is good
    • Two major mountain passes or the choice to leave Colorado, go to Wyoming and drive back into Colorado so you only have to deal with one major mountain pass (adding on 30 more miles)

Many specialists come up to mountain communities on a rotational basis.  However, this may be once a month and possibly less frequent.  Telehealth is the obvious stop-gap for patients in rural and mountain communities that need specialized care.  A barrier to telehealth visits as Dr. Lin has mentioned in his blog has mostly been the providers.  However, with social distancing and with CMS lifting restrictions on reimbursement for telehealth, providers quickly adopted telehealth to keep revenue streams flowing for their practices.

Telehealth and Telemedicine Expansion and Deregulation

Telehealth and telemedicine rules and regulations relaxed at the start of the COVID-19 pandemic.  Now is the time to figure out how else to utilize technology to improve healthcare delivery.  Now is the time for innovation and policy reform.  So, how can telehealth help patient advocates and family members?  Could it be the answer for me and my dad’s visit?  Will it work for others in an urban setting or family members that are geographically separated?

Being a Patient Advocate Remotely

Before the pandemic, I had planned on taking a day off of work to drive down to Denver to accompany my father to an appointment at a neurology clinic.  This appointment transitioned to a telehealth visit following the outbreak.  I considered making the two-hour drive from Steamboat Springs to Fraser to be with him for the appointment.  After all, I would generate a net gain of two and a half hours from not having to drive all the way to Denver.  In a moment of clairvoyance, however, I decided to find out if I could join remotely.  After working with a few key stakeholders at UCHealth, we discovered that if my father gave me access to his My Health Connection account, I could join the same way he would for the remote visit.  This access also allowed me to review my father’s medications as the provider discussed them with my mom and dad and access the summary notes from the visit, so I could discuss treatment options with him and my mother at a later time.

The Visit (that’s me at the bottom, by the menu bar)

It was strange to know that I would be on a video call with my parents, but to be on the phone with them as well, ensuring that they could log on.  My wife and I have discussed the shift in caring for both sets of aging parents, but this was the first time I needed to support them on multiple fronts.  First working with them on technology and second being a health advocate.  The visits felt distant, yet at the same time normal.  The medical assistant greeted us virtually and started the intake process.  Dr. Zachary Macchi jumped onto the call about five minutes in and reviewed history and started the evaluation.  About twenty minutes into the call, Dr. Samantha Holden was able to join as well.  In the span of twenty minutes a total of six people (including my father) were working together.  Had we all gone down to Denver together, this may have been the same outcome.  However, Dr. Macchi joined the call first to help Dr. Holden.  He stated right away that she would be able to join us, but had other commitments.  My guess is that if we were in a traditional setting, we would have waited an extra 20 minutes but telehealth gave the flexibility for coverage.  Telehealth has its limitations.  My father had difficulty following the motor skills test.  We were unsure if it is his motor function or his ability to follow a two dimensional image in the three dimensional world.  For this and other reasons, everyone agreed on an in person visit three months following the virtual visit.

Just the first step… what are the next.

This visit made me realize the opportunity for telehealth in the patient advocacy realm.  While telehealth offers a convenience for the patient, it certainly helps with obstacles that patient advocates face.  I am lucky to live just a few hours drive from my parents.  If I lived outside of Colorado, I doubt I would be as involved in their care.  However, we now have the tools to improve care coordination between family members.  Our first step needs to be promoting the technology to allow for remote patient advocacy.  However, we could take it even further.  What if we could have an MA set up a camera during an in-clinic visit so the advocate (or family member) could join the visit if they lived too far away to join in person?  What are the other ways to utilize telehealth for family members and patient advocates?  Will CMS go back to restricting reimbursement for telehealth?  Time will tell for these questions, but we need the health care community to (dare I say) advocate for telehealth and the access it can bring for patient advocates.

cid:image005.png@01D645B2.ED7150D0
Guest Blogger Glenn Sommerfeld (thank you!)

Doomscrolling. Are you guilty of it? (nytimes)

image from the NYtimes article

https://www.wired.com/story/stop-doomscrolling

Here is a new term for you: Doomscrolling. I am guilty of this, until I become aware of it and have to wrench myself away. It is a like car-crash in slow motion and you want to know how this horror story ends.

CMIO’s take? STOP. Turn it off, go live your life, and talk about
THREE good things.

EHR v Covid-19. What age groups do well with Video Visits?

Time for more data surfing! UCHealth’s overall visit volume (including in-person and video visits and scheduled phone visits) have recovered about 80-90% of pre-pandemic levels.

Today, we’re looking at visit volumes among different age groups of patients. Keep in mind, UCHealth is primarily an adult hospital. Our partner, Childrens Hospital of Colorado, sees most of the pediatric population regionally. We do have some pediatric practices, and of course our extensive family medicine primary care practices also see pediatric patients. This will explain the low volume of pediatric visits below. On the other end, only 3.9% of UCHealth patients are over age 85.

So, what happened to visit volume with each of these age groups?

Turns out, the curve for EVERY age group is similar! Green is age 40-65 and about 1/3 (our largest fraction) of our patient population. Fuchsia is 65-85 and our second largest, purple is 18-40, orange is under 18, red is over 85. The curves start at different points, but follow the same trajectories. That divot on the right side is Memorial day, clinics closed, so 4/5 of the weekly volume that week.

Here is the Home telehealth Video Visit volume! Some interesting findings here. You notice that fuchsia and purple switched places, meaning that a much higher proportion of 18-40 year old patients chose Video Visits compared to 65-85 year old patients. All the other curves stayed in their relative positions. Furthermore, EVERY age group had a proportional bump up in video visits, even those over 85! Finally, the video visit curve is falling back, about to 50% of the peak (so far). It will be interesting to track this in the coming month or 2 and see where we end up, after in-person visits are fully ramped up again.

CMIO’s take? Who knows? Another example to show that we are going to bed with a cliff-hanger every night. I wonder what happens next. The good news: I’m feeling good about having a better handle, even after a few short months, of what Covid-19 can throw at us. Ain’t data cool?

EHR v Covid-19. Where’s Covid now? And, patient care is already looking different!

Covid-19 RNA positive tests at UCHealth in purple

We are well into our fourth month of this pandemic. Looking at our graph, purple shows influenza B peaking in December, influenza A peaking in February, and leaving aside an artifactual spike in mid March, when we started co-testing for major respiratory viruses at the same time we started testing fro Covid-19 in earnest, all other viruses have dissipated. Then you see this impressive bump in Covid-19 illness, peaking in mid April, in our organization. Keep in mind, this is just POSITIVE tests for Covid-19 RNA in patients seen at UCHealth. Because we care for 1.9 million patients in Colorado, though, it is a reasonably large population sample. Furthermore, Covid-19 tests were SCARCE prior to mid March, and numerous patients were likely developing Covid symptoms in February (see below).

Along left edge, top to bottom lines: In-person visits, online patient messages, phone calls, video visits, scheduled phone visits

So, how has this affected our visits and our telehealth efforts? Purple shows you the dramatic dip with in-person outpatient visits, and the gradual climb back toward baseline. Then there is the green line of home telehealth video visits, going from nearly nothing to about 20,000 weekly in early to mid March, with gradual falling off in the past 8 weeks and it seems we might stabilize near 10,000 visits weekly. This is still about 100x the volume of video visits prior to the pandemic.

Then there are the other trend lines that are interesting: Red is the ongoing volume of Patient messages before and during the pandemic. Leaving aside the bump in mid May (not sure why: perhaps related to a system-broadcast), our baseline of 22,000 messages per week increased to 30,000, about 33% increase in volume, starting to rise on Feb 22. This pre-dated by THREE WEEKS the steep decline of in-person visits and the upswing of telehealth visits on Mar 14, and the Colorado Stay at Home order of Mar 26.

Even more interesting: telephone volume in blue, saw a tiny bump on Mar 14, but then was unchanged during the entire period. By contrast, in fuchsia Scheduled telephone visits (billable as of mid March per CMS rules), appeared in early April.

In one graph, you can see: online patient messaging demand scaling up, phone calls being static, scheduled phone calls appearing when billable, on top of the change for in-person and video visits.

Some hidden factors at work here: UCHealth set up a Covid-19 nurse advice line; those calls are not visible on any line in this graph, and those hard-working nurses took tens of thousands of calls from Coloradoans (not just UCHealth patients).

So, this data dilettante has to ask, could an increase in online patient messaging (regardless of content of message) be another possible leading indicator for future pandemic surges? We can’t be sure if these messages were about general anxiety, Covid symptoms, or perhaps completely unrelated, but it is suspicious that there is a sustained increase in volume of messages by 30%+ since mid-March. On the other hand, why isn’t online message volume falling, like home telehealth visits are falling, now that clinics are opening up in-person appointments? Stay tuned!

The open question now is: what will CMS (Centers for Medicare/Medicaid Services) do with paying for Video visits and scheduled Telephone visits? Will those payments stop or scale back? This will certainly affect all health systems still heavily relying on Fee for Service, until the rise of Value Based Care (insurance plans paying for Quality instead of Volume) takes over.

CMIO’s take? These are unprecedented times, and patient behavior and health system behavior is fascinating. A tiny RNA virus has changed the way (phone, online, in-person) patients and healthcare providers interact. What comes next?

Ortho Virtual Care. Ukulele song about video visits (parody of Wonderful World)

Nope, did not use the word “pandemic” or “Covid”.

Searching Youtube for “Covid songs” gets you this: https://www.youtube.com/results?search_query=covid+song

Which is an entirely unreasonably long list; there are some great selections there. I’ll leave you to browse.

During pandemic, I’ve been learning clawhammer style, from this guy:

Makes my uke sound more like a banjo. Weird, and cool.

Meantime: Our clinics are getting back to business; our patients are returning to in-person care, our visit volumes are back up, past the 80% mark. I hope you are all staying safe; we’re not out of this yet, but it is starting to feel less like a sprint and more like a marathon. Take care of yourself, get some exercise, bring back a hobby or two.

EHR v Covid-19. Leading Indicators: work-in-progress, VERSION 2

From nounproject.com. Leading indicators may seem like astrology, and tarot cards. BUT there is science too!

Thanks to those of you who caught my non-displaying graph images, I’m reposting now converting my original PNG to JPG. Please let me know if you can see these and follow the reasoning below! (edited 6/15, CTL)

—–

Thanks to Brendan Drew, one of our data scientists, who is diving into the analysis of Leading Indicators, for the graphs and reasoning below. If I can twist his arm for more graphs, will pass them along.

If you recall, I discussed this recently: the idea that, our future is uncertain. Even though we have survived the first wave of the Covid-19 pandemic, we are concerned about possible future waves. How might we prepare?

If you don’t know this about me already, I find “making the sausage” in informatics and data science fascinating. Here are some intermediate steps we are taking beyond my “data dilettante” days as we search for signal in the noise.

These are all COVID-19 new codes. Firstly, note that ORANGE line R68.89 , orange shows up WAY before March. Turns out, this is not only “suspected Covid-19” it is also “Other Symptoms and Signs” previously in the ICD10 dictionary. So, that is a terrible signal. Then, RED line Z20.828 “Close exposure to COVID-19” is also “Exposure to influenza”. Hmm. Then, BLUE line B34.2 “Coronavirus Infection” is also “Coronavirus, unspecified.” Also Hmm. Only GREEN line U07.1 “Coronavirus identified” is highly specific for COVID-19 in the graph.

So, how do we make sense of this?

First, we take ONLY hospital patient codes for CONFIRMED (BLUE) versus SUSPECTED Covid (ORANGE), and we see that the BLUE CONFIRMED line shows two peaks, whereas ORANGE, there is no real signal there at all. GREEN is adjusted for Market Share based on 2019 data for that zip code (we are trying to localize prediction to the Zip code level).

Now, we compare zip codes. Blue line is 80011, Aurora near University of Colorado Hospital, a relative hot spot in Denver Metro region, and orange is 80634, the hot spot near Greeley hospital, and we see a temporal difference in the onset and peak of Greeley being earlier than Aurora. Interesting.

Here is where it gets tantalizing, and we have to hold back our excitement: Pair up the outpatient symptom data with the inpatient hospitalization rate for Confirmed Covid. Here it is for Aurora, x-axis lined up by date:

Those of us who cannot contain our excitement will see a visual rise in RED (outpatient symptoms suspicious of COVID, like fever, cough, shortness of breath), in the 80011 zip code increasing about 2 weeks BEFORE the corresponding rise in COVID-19 cases at University of Colorado Hospital in Aurora (also 80011). We WIN! Right?

Also, here’s the corresponding graph for Greeley:

This is a bit messier: what is that symptom peak in February? There is no corresponding COVID hospitalization peak in Feb/Mar. BUT, the symptom peak in mid March DOES correspond to a rise and peak in late March, and all of April.

My theory: mid February was probably Influenza A, and we did NOT track hospitalizations on our graph for that, AND the COVID confirmed codes did not get implemented until mid March, and maybe NOT attached in retrospect to patients who MIGHT have had COVID, but were admitted BEFORE those codes went into effect. This is harder than it looks!

Are you looking for a final answer? SORRY! We are still cranking away at this. Even though we humans have frontal lobes that CANNOT WAIT to see patterns (even where there is no pattern!), we have to resist that urge. AND, how do you teach an algorithm (even if there IS a pattern here), to tell us: YES you should pay attention to THIS rise in the data, but THAT ONE is just random noise.

For example, imagine the 80011 graph prints out one day at a time, moving to the right. At what point, would you tell the algorithm to alert us: YES it is TIME TO BRING IN MORE DOCS AND STAFF FOR THE NEXT SURGE.

Would it be: March 15, when there is an uptick? But there are lots of upticks just like that. March 22, a week later, when the line is DOUBLE of the average from 0.0007 to 0.0014?

AND, worse yet, UCHealth is only one of 5 health systems in Metro Denver and across the state of Colorado. Will cases come to US or to other health systems? What will the peak be? Will it be a tiny peak? (Hey, CT, why did you call all of us in here for these dozen patients?) Will it be a HUGE peak (Hey, CT, you didn’t raise enough of an alarm, there still aren’t enough of us).

Finally, signal to noise MIGHT be easier for the summer months when Influenza is done, but what about the fall when Influenza B and many other viruses are back in action? What about seasonal allergies during spring and summer that might kick off cough and shortness of breath?

CMIO’s take? Figuring out Leading Indicators is HARD. If YOU have this figured out, let us know. We’re still working on it. But the math and the figuring-it-out is pretty fascinating in the mean time.

All y’all EHR-using folks don’t know how good you have it.

The author, Chief Medical Information Officer of 3 million paper medical records.

For fun, I’ve set my Zoom background with an actual vintage 1997 photo I took of the medical records room in the basement of University of Colorado Hospital on Ninth Avenue in Denver (back when giants walked the earth). This aisle featured 6 stacked rows of medical record charts AND piles of paper record folders ON TOP since we were out of room (not shown). This was one of 29 aisles of records in the Records Room, holding ONLY the latest 3 years of records: the rest were retained (for 27 years) in a downtown warehouse.

Fun fact: we turned down lots of innovation partnerships and offers of free services because the medical information locked in those paper records was too difficult to pull out:

  • We have a Pulmonary Function mobile van parked out front: send us all your patients who currently smoke and we will screen their lung function for free!
  • Hey, our insurance company will pay you a bonus payment if you can prove all of the patients who have had a previous heart attack are taking aspirin! (true story, a clinic trying to prove this using paper medical records and clerical staff paid more gathering the data than they received in bonus money)
  • Quick: the mobile mammogram bus is coming next week: let’s call all our patients who are due for mammography screening!
  • We have a new diabetes educator visiting for a couple weeks! Can we contact all our patients with diabetes to come for a free visit?
  • Uh, oh! The medication Bextra is being recalled by the manufacturer; quick: call all our patients taking that medication! (True story: 1/2 of our clinics were able to run a report on our EHR at the time and call affected patients immediately; the other half, still relying on paper records, had to say… “well, when the patient calls for a prescription refill in a few months, THEN we’ll tell them…”)

Fortunately, it is simple in our current EHR to run ad-hoc reports to do all this now. Whew! And, we can do predictive analytics on this data to save lives that would have blown my mind back then.

Here’s another flashback:

Don’t tell the post office; this is where all their bins went

THIS is the Medical Records intake room, back when we were ONE hospital, 40 clinics (we’re now 12 hospitals, 800 clinics). On average, 6 vertical feet of paper, received EVERY DAY. Fifty medical records staff, filing, sorting, pulling, sending, receiving, creating new charts. And, still, we were 2 WEEKS behind on filing.

We had over 20 transcription services, all local, receiving tiny tape-recorder dictaphone tapes, transported by COURIER from the doctors dictating. As an aside, some of us remember hearing doctors mumbling their ultra-fast, only partly understandable dictations walking the halls between patients. On average, outpatient transcriptions took about 2 weeks to complete and print out, mail, and file back into the record. Inpatient daily transcriptions were ordered STAT for 3x the cost and typed same day, arrived by urgent courier in the late evening and taped into the paper chart.

I am proud of my doctor handwriting

For the record, here’s a paper progress note I wrote in 1999 on “non-carbon paper” sending the original copy to Hospital Medical Records, and then keeping the yellow copy in a “shadow chart”: a duplicate set of medical records kept in our “off-site clinic” because … we could not count on Hospital Medical Records to pull the relevant charts for clinic patients scheduled each day.

Don’t even get me started on our appointment scheduling system. “Oh yes, thanks for calling! So you’re looking for Dr. Lin’s next available appointment? Sorry, nothing for the next 3 weeks. Oh, you’d like to see the next available doctor? =sigh= OK I’ll pull down the other twelve 3-ring binders, one for each doctor, and see who might have an open spot.”

Are you keeping track? 50 medical records staff at the hospital to maintain Main Medical Records, and 1-2 additional medical records staff at EVERY clinic (about 40 clinics) to keep a shadow chart. Because we don’t trust each other to keep track and deliver records on time!

Hello, dolly

Oh, and meet this guy. In 1997, our medical information (see: x-ray films, paper medical records, dictaphone tapes) moved at the speed of rush-hour traffic on Colfax Avenue. Seven miles each way, 12 leased buildings throughout metro Denver. Two round trips every day.

With all this person-power and effort, the result? On a typical clinic day, I would see about 18 internal medicine patients. Main medical records would successfully deliver charts for about 9 patients. Our clinic’s shadow chart system would deliver charts to my exam room for about 6 additional patients, leaving, on average THREE patients with NO CHART. Just a piece of non-carbon paper, with handwritten vital signs and a list of patient-reported allergies that day. Mind you, there was no such thing as a clinical computer system at the time. As a result:

“Hi Doc! It is great to see you! What did my cardiologist tell you about me when he saw me 2 weeks ago and did all those tests? He said that I should come talk to you about his report.”

Um. I don’t have any of your records today. I see your blood pressure looks good and that you report no allergies to medicines though.”

“What?! I made this appointment to go over his report! That visit was 2 weeks ago!”

“Yes. Um. What condition, exactly, do you have? Why did we send you to my cardiology colleague? What do you remember that he told YOU? Can you help me out here?”

“This is disappointing. You mean you really have nothing on me? Do you at least have the blood test results or the echo result?”

“Um, no. I’m really sorry about this. Okay, tell you what, no charge for today, my apologies for wasting your time and I will call you later this week after I call and yell at my medical records people and maybe get your chart and see what it says.”

“Whatever. You guys should really get your act together. Okay, can you at least go ahead and refill those 3 medicines that you prescribed for me from last year? I’m about out.”

(Excitedly taking out prescription pad) “Sure, I’m happy to! Do you happen to remember the names of the medications and the doses and what they’re for?”

Let’s not even talk about loading up a 2-foot-tall stack of medical records in our arms, walking out to the car, throwing them in the trunk, driving home and dictating late into the night, and hopefully remembering to bring them back into the office the next day.

And, if there was an urgent need for a particular medical record? We would routinely have a couple staff members wandering the clinic, from office to office, desk to desk asking: “Do you have the chart for Peterson, Mary, or Smith, Joseph, or Samuels, Jane?” and thus not answering the phone, or rooming patients…

Of course, by contrast, with our current EHR, tap-tap-tap: instant access to any patient record.

Yesterday, for example, my patient met her oncologist to discuss a new diagnosis of metastatic cancer. Today, I was able to read her consulting note, review the pathology from a recent biopsy, refresh my education about peritoneal carcinomatosis in an EHR-linked online textbook, secure-chat and then phone call with the oncologist about prognosis and treatment options, set up a video visit with the patient and her family, and have a have a well-informed, thoughtful conversation about her next steps.

This speed and coordination would not have been possible in the era of paper charts.

Not as cool as Jimmy Fallon’s Thank you Notes

Wait! One more thing! Remember the good old days when we received faxed blood test results and then had to notify patients by writing a STACK of folded post cards? I faced a stack of these EVERY EVENING at the end of clinic. Please don’t ask me how many times a patient brought back a post card saying: “Um, this looks pretty important, but, I think you meant to send this to a different Peter Smith. I haven’t had a blood test in awhile.”

Yikes.

Our patient Portal, we call My Health Connection: we release test results to the patient online, and then send comments with our interpretations, arriving to the patient’s inbox instantly. Comment from my patient? “It feels like I have my doctor in my pocket. So cool.”

CMIO’s take? All y’all don’t know how good you have it.

On the other hand, are you old, like me? Do you remember those days?

On the third hand, in another decade, I hope folks will look back to TODAY and marvel how much better the future is.