Further developing the care model – part 2 – definitions

We’ve gone about as far as we can go in theoretical terms with the process model.   The next step is to create a training data set on which to do further experiments and get further insights about combining process and statistics.

Let’s define the variables and the dataset we will be using for this project.

1.  Each encounter with the entire process (all sub-processes from start to finish) requires a unique identifier (UID).   A single patient could go through the process more than once, so a UID is necessary.  This can be as simple as taking their MRN and adding a four digit trailing number identifying how many times through the process.

2.  For each sub-process, time is measured in minutes.  Using start and stop times/dates has some added benefits but is more complex to carry out, as anyone who has ever done so will recognize (non-synced internal clocks providing erroneous time/date data, especially after power outages/surges).

3.  The main times are the pathway times – Sunprocess 1-2, 2-3,3-4,4-5,5-6.
1-2 Reflects the time it takes the physician to order the study and patient transport to come for the patient.
2-3 Reflects transport time from the ED to CT holding.
3-4 Reflects time of nursing evaluation of the patient’s appropriateness for CT imaging.
4-5 Reflects the time bringing the patient into the imaging room and scanning, and sending the study to the PACS system.
5-6 Reflects the time for the radiologist to react to the study being available, interpret the study, and dictate a preliminary result in a format the ED physician can use.

4.  When an interaction occurs along the inner lines we need to account for these in a realistic way.  The boolean variable built into the process will take care of whether the interaction is present or not.  The effect of the off-pathway interaction is to lengthen the time of the main pathway sub-processes.  For example:  Patient arrives in CT holding and nurse identifies a creatinine of 1.9 which needs further information for contrast imaging.  She phones the ED doctor (4 min) and then phones the Radiologist to approve the study based upon that information (2 min).  These phone calls are part of the overall time in subprocess 3-4 for this UID.   To evaluate the time process 3-4 takes without these phone calls, simply subtract the two inner processes.
Or in other words, Process3-4(theoretical)=Process3-4(actual)-(Process1-3 + Process 3-5)

5.  This table will represent potential times for each part of the process, chosen at random but with some basis in fact.

 

Process Mean Time Variability
1-2 10 minutes – 5 / +30 minutes
2-3 15 minutes – 5 / +10 minutes
3-4 15 minutes – 10 / + 15 minutes
4-5 15 minutes -5 / +30 minutes
5-6 20 minutes -10 /+40 minutes
1-3 5 minutes – 3 / +10 minutes
1-4 5 minutes – 3 / +10 minutes
1-5 5 minutes – 3 / + 10 minutes
3-5 5 minutes – 3/ + 10 minutes
3-6 5 minutes – 3/ + 10 minutes

Next post, we’ll begin coding this in an R language data frame.

Quick Post on Systems vs. Statistical Learning on large datasets

"Bp-6-node-network" by JamesQueue - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons - https://commons.wikimedia.org/wiki/File:Bp-6-node-network.jpg#mediaviewer/File:Bp-6-node-network.jpgThe other day I attended a Webinar on Big Data vs. Systems Theory hosted by the MIT Systems design & management group which offers free, and usually very good, webinars.  I recommend them to anyone interested in data driven management using systems and processes.  The specific lecture was “Move Over, Big Data! – How Small, Simple Models can yield Big insights” given by Dr. Larson.  The lecture was very good – it discussed some of the pitfalls we might be likely to fall into with large data sets, and how algorithmic evaluation can alternatively get us to the same place, but in a different way.

Great points raised within the lecture were:
Always consider the average as a distribution (i.e.  a confidence interval) , and compare to its median to avoid some of the pitfalls of averages.
Outliers are easy to dismiss as noncontributory – but when your outlier causes significant effects on your function (i.e. ‘black swans’) you’d better include it!
Averages experienced by one population may be different than averages experienced by another.  (a bit more sophisticated than the N=1 concept)

There was a neat discussion of Queues with Little’s law cited – L=lambda W where L=time average # of customers in system, lambda is average arrival rate and W- mean time spent by customers in the queue.  M/M/K queue notation cited.  Dr. Larson’s Queue Inference Engine (using a poisson distribution) was reviewed.  You can find some more information about the Queue inference engine here.  The point was that small models are an alternative means to sassing out big data than simply using statistical regression.  I’ll admit to not knowing much about queue theory and Markov chains, but I can see some interesting applications in combination with large datasets.  Much along the lines of an ensemble model, but including the queue theory as part of the ensemble…  Unfortunately, as Dr. Larson noted, much like in the linear models we have been approaching, serial queues or networked queues require difficult math with many terms.   The question yet to be answered is – can we provide the best of both worlds?

Further developing the care model – theoretical to applied – part 1

Consider an adult patient who has presented to the ER for abdominal pain.  The ER doctor suspects an appendicitis, so next is a CT scan to “r/o appendicitis.”  There is an assumption that the patient has already had labs drawn and done upon presentation to the ER (probably a rapid test).ER_CT_process

First, the ER doctor has to decide to order the CT study, and write the order.  We’ll assume a modern CPOE system to take out the intervening steps of having the nurse pick up the order, sign off, and then give it to the HUC to call the order to the CT technologist.  We’ll also assume that the CPOE system automatically contacts patient transport and lets them know that there is a patient ready for transport.  Depending on your institution’s HIMSS level, these may be a lot of assumptions!

Second, patient transport needs to pick up the patient and bring them to the CT holding area (from the hallway to a dedicated room).

Third, the nurse (or a second technologist / tech assistant) will assess this patient and make sure that they are a proper candidate for the procedure.  This involves taking a focused history, making sure there is no renal compromise that would be made worse by the low osmolar contrast (LOCA) used in a CT scan, ensuring that IV access is satisfactory for the LOCA injection (or establishing it if it is not), and ensuring that the patient does not have a contrast allergy that would be a contraindication to the study.

Fourth, the CT technologist gets the patient from holding, places them on the CT gantry, hooks up the contrast, and protocols the patient, and then scans.  Once the scan finishes, the patient returns to holding, and the study posts to the PACS system for interpretation by the M.D. radiologist.

Fifth, the radiologist physician sees the study pop up on their PACS (picture archiving & communication system), interprets the study, generates a report (usually by dictating into voice recognition software these days), proofreads it and then approves the report.  If there is an urgent communication issue, the radiologist will personally telephone the ER physician, if not, ancillary staff on both sides usually notice the report is completed and alerts the ER physician to review it when he has time.

Sixth, the ER physician sees the radiologist’s report.  She or he then takes all the information on the patient, including that report, laboratory values, physical examination, patient history, and outside medical records and synthesizes that information to make a most likely diagnosis and exclude other diagnoses.  It is entirely possible that the patient may go on to additional imaging, and the process can repeat.

In comparison to the prior model where all interactions were considered, we can use a bit of common sense to get the number of interacting terms down.  The main rate limiting step is the ordering ER physician – the process initiates with that physician’s decision to get CT imaging.  It is possible for that person to exceed capacity.  Also, there are unexpected events which may require immediate discussion and interaction between members of the team – ER physician to either radiology physician, radiology nurse, or radiology technologist.  Note that the radiology physician and the radiology nurse can both interact with the ER physician both before (step 1) and after (step 6) the study, because of the nature of patient care.

An astute observer may note that there is no transport component of the patient back to ER from radiology holding.  This is because the patient has already been assessed by the ER physician, and more testing, disposition, etc… is pending the information generated by the CT scan.  While the patient certainly needs care, where that care is given during the assessment process (for a stable patient )is not critical.  It could be that the patient goes from CT holding to dialysis, or another testing area, etc…  Usually the next ordered test, consult, or disposition hinges on the basis of the CT results, and will be entered via CPOE, where the patient and ER physician need not be in the same physical space to execute.

From practical experience, ER physician – CT technologist interactions are most common and usually one-sided.  (please take this patient first, I want the study done this way, etc…)  ER physician – nurse interactions are uncommon and usually unidirectional (nurse to physician – this patient is in renal failure, we can’t use LOCA, etc…).  ER Physician and radiology physician interactions are even less common but bidirectional.  (‘This patient is confounding – how can we figure this out?’ vs. ‘your patient has a ruptured aortic aneurysm and will die immediately without surgical interaction!’)

Next post we will modify our generalized linear model and begin assembling a dataset to test our assumptions.