Diagnostic time out

What is a diagnostic time out? Succinctly defined, it’s a deliberate exercise in differential diagnosis and systematic clinical reasoning in the care of an individual patient. But wait, I hear someone say… isn’t that what we do already? Well, no. We’re all familiar with the traditional model for clinical reasoning that we’re taught in medical school but those of us in the real world of practice nowadays, if we’re honest, realize that it seldom happens. There’s just not enough time when you’re forced to see too many patients each day. And hospitalist incentives, with their emphasis on speed and quick adoption of specific diagnostic labels, run in opposition. What do we as hospitalists do instead? Well, aside from all the care pathways and metric incentives that tell us what to do, we rely on clinical instincts and rules of thumb. Because they bypass formal analysis, they save time. They serve as cognitive shortcuts. We call these heuristics. This method of thinking (fast, instinctive, intuitive) is sometimes known as system 1 thinking. It has the advantages of being efficient and fast and sometimes, in critical situations, life saving. But it comes at the cost of a certain error rate. In order to better understand the process of system 1 thinking we have given the various heuristics names and categories. I recently listed some of those in this post. 

If system 1 is our usual measure of processing to get around time constraints the alternative is system 2: formal clinical reasoning .  System 2 thinking was the topic of a recent paper in CriticalCare Clinics. Although based on a survey of people working in a NICU the article has general applicability. The authors contrast system 1 and system 2 thinking in this manner:

Dual process theory holds that individuals engaging in medical decision-making use one of 2 distinct cognitive processes: a system 1 process based on heuristics – the use of rapid pattern recognition and rules of thumb – or a system 2 process, based on deliberate analytical modeling and hypothesis generation. While invoking system one processes individuals can think fast and reflexively and can even operate at a subconscious level, using pattern recognition to sort vast amounts of clinical information quickly before an illness script that allows for the rapid elaboration of a differential diagnosis. In contrast system 2 processes require focused attention and are purposefully analytical, relying on deliberate counter-factual reasoning to generate hypotheses regarding the pathophysiologic mechanisms by which a patient’s symptoms are produced.

The authors introduced the concept of the diagnostic time out to describe this shift of thinking because it requires deliberate effort. It’s not going to arise spontaneously in the natural course of the ward routine. (The authors were not the first ones to use this term). The diagnostic time out can be considered the cognitive equivalent of the better known procedural time out.

Why is a diagnostic time out needed? Research on diagnostic error has indicated that while some instances are due to system problems (such as failure to communicate test results) most are cognitive errors. These can be linked to the heuristics of system 1 thinking. The diagnostic time out, or the deliberate exercise of system 2 thinking, is a way to complement these cognitive shortcuts with a more analytical process.

Some opinion leaders in the field of diagnostic error have suggested universal adoption of system 2 thinking. This is problematic due to time constraints. Besides, there are some essential benefits of system 1 thinking, particularly in acute life-threatening situations. The real trick is how best to selectively employ system 2 thinking. In other words what are the situations in which system 2 thinking should be used? The authors suggest handoff situations in complex patients including ER to hospitalist, off service/on service and ICU to ward transfers.

How does it work? The authors propose a template but it’s really just the traditional clinical reasoning process. One of their points really got my attention: during the time out diagnostic labels should be removed and replaced by signs, symptoms, manifestations and clinical concerns. This of course is the opposite of what your coders and hospitalist leaders want you to do.

What are some of the barriers to implementation? In addition to time constraints, fear of ambiguity is an important factor. We are afraid to admit what we don’t know. One thing you will never hear a hospitalist say out loud is “I’ll have to think about that.”

The fight to curb antimicrobial resistance: how are we doing?

 

From a recent NEJM review on this topic:

In November 2019, the CDC released an updated version of its antibiotic-resistance report…

The new report reveals reductions in the incidence of infections caused by carbapenem-resistant acinetobacter species, multidrug-resistant Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococcus, and drug-resistant candida species. In addition, it identifies an increasing incidence of Enterobacterales that produce extended-spectrum beta-lactamase and drug-resistant Neisseria gonorrhoeae infections and the emergence of the multidrug-resistant yeast Candida auris.

Adverse drug reactions in the elderly: a clinical vignette and a reminder of the Beers list

The free full text article is here. The newly revised Beers list can be accessed here.

The adverse effects of CPOE on ER throughput

Discussion at SGEM and primary source.

Checking urine eosinophils to evaluate for acute interstitial nephritis

I agree this is one we should probably just stop doing. Not that it’s costing a lot of money or harming patients, but, despite popularity for years, the test characteristics according to recent data are so poor it’s probably just not worth doing.

Unintended consequences of patient safety interventions

On the whole there is little evidence that patient safety initiatives at the system level have been beneficial. Here is a systematic review unintended consequences. From the review:

Abstract: This is a systematic review of the literature on unintended consequences of clinical interventions to reduce falls, catheter-related urinary tract infection, and vascular catheter-related infections in hospitalized patients. A systematic search of the literature was conducted in CINAHL and PubMed. We developed a screening tool and a two-stage screening process to identify relevant articles. Nine articles met inclusion criteria, and of those, 8 reported on interventions to reduce patient falls. Four studies reported a positive, unexpected benefit; 3 studies reported a negative, unexpected detriment; and 4 reported a perverse effect (different from what was expected). Three studies reported both positive and perverse effects arising from the intervention. In 4 of the studies, despite fall prevention interventions, patients fell while trying to get to the bathroom, suggesting that interventions to reduce one adverse outcome (i.e., CAUTI) may be associated with another outcome (i.e., patient falls). In some cases, there were positive outcomes for those who implemented and/or evaluated interventions. We encourage colleagues to collect and report data on possible unintended consequences of their interventions to allow a fuller picture of the relationship between intervention and all outcomes to emerge.

These represent the safety areas where Medicare has focused its “no pay for errors” policy.

Sep 1: thumbs up or thumbs down?

A piece in Today's Hospitalist covers some of the ins and outs of CMS's most complicated core measure yet. we’ve yet to realize the unintended consequences. It’s based on data from a survey (and the subjects were “quality officers” and others predisposed to drink the performance kool aid) the results of which suggested that the measure is perceived to be beneficial. But it restricts clinical judgment and is based on ideas deemed out of date by many.

After going through a long list of flaws and potential harms of the measure here's how the Today’s Hospitalist piece concludes (emphasis mine):

“While some people’s instinct is to just reject” the measure, Dr. Barbash says he draws a different conclusion from his research. “We have a professional obligation to try to make it better in ways that ultimately help us provide the best care for patients.” While SEP-1 “has gotten us to start paying attention to the most important killer of hospitalized patients,” he believes a revised sepsis measure could do better.

He's asserting that it took a CMS core measure to even get us to start paying attention to sepsis. Where has he been the last 15 years?

Fighting C diff: the case for gastric acid suppressant stewardship

A meta-analysis of observational studies suggests it might decrease the risk of Cdiff recurrence.  This includes PPIs and H2 blockers.

Sepsis alerts by the EMR failed to improve outcomes

Yet another “systems improvement” that has failed to live up to the hype. Sepsis harassment.

Rapid response calls are fewer in the middle of the night, mortality spikes at 7 AM

That means rapid response systems save lives, right?

No, not in my reading of the study. Here’s a summary of the findings:

Objectives: Decreased staffing at nighttime is associated with worse outcomes in hospitalized patients. Rapid response teams were developed to decrease preventable harm by providing additional critical care resources to patients with clinical deterioration. We sought to determine whether rapid response team call frequency suffers from decreased utilization at night and how this is associated with patient outcomes.

Design: Retrospective analysis of a prospectively collected registry database.

Setting: National registry database of inpatient rapid response team calls.

Patients: Index rapid response team calls occurring on the general wards in the American Heart Association Get With The Guidelines-Medical Emergency Team database between 2005 and 2015 were analyzed.

Interventions: None.

Measurements and Main Results: The primary outcome was inhospital mortality. Patient and event characteristics between the hours with the highest and lowest mortality were compared, and multivariable models adjusting for patient characteristics were fit. A total of 282,710 rapid response team calls from 274 hospitals were included. The lowest frequency of calls occurred in the consecutive 1 AM to 6:59 AM period, with 266 of 274 (97%) hospitals having lower than expected call volumes during those hours. Mortality was highest during the 7 AM hour and lowest during the noon hour (18.8% vs 13.8%; adjusted odds ratio, 1.41 [1.31-1.52]; p less than 0.001). Compared with calls at the noon hour, those during the 7 AM hour had more deranged vital signs, were more likely to have a respiratory trigger, and were more likely to have greater than two simultaneous triggers.

Conclusions: Rapid response team activation is less frequent during the early morning and is followed by a spike in mortality in the 7 AM hour. These findings suggest that failure to rescue deteriorating patients is more common overnight. Strategies aimed at improving rapid response team utilization during these vulnerable hours may improve patient outcomes.

I have no data but my strong subjective impression is that this diurnal pattern existed long before anybody thought up the idea of rapid response teams. Hospital resources are slim at night and are most readily available mid day. 7 AM is shift change in most hospitals. There are a lot of confounders here.

Protocols, pathways and sets of core measures: dare we question them?

Here’s a great article in Annals of Intensive Care.

It’s actually a gem of an article.

Pathways, protocols, core measures, bundles and check lists

Here’s an appropriately critical view.

The EMR and other “systems improvements”: unfounded optimism

Despite repeated failures, we are on the cusp of positive results according to this piece.

Give doctors tools, not rules

Matthew Hahn on the phony baloney of “value based” care.

Lumbar puncture in patients on dual antiplatelet therapy

Reasonably safe in this small study.

Tell me something I didn’t know: More expensive care isn’t necessarily better care

A JAMA Internal Medicine study.

Medical errors in nursing home patients

From a systematic review:

Medication errors (MEs) result in preventable harm to nursing home (NH) residents and pose a significant financial burden. Institutionalized older people are particularly vulnerable because of various organizational and individual factors. This systematic review reports the prevalence of MEs leading to hospitalization and death in NH residents and the factors associated with risk of death and hospitalization. A systematic search was conducted of the relevant peer-reviewed research published between January 1, 2000, and October 1, 2015, in English, French, German, or Spanish examining serious outcomes of MEs in NHs residents. Eleven studies met the inclusion criteria and examined three types of MEs: all MEs (n = 5), transfer-related MEs (n = 5), and potentially inappropriate medications (PIMs) (n = 1). MEs were common, involving 16–27% of residents in studies examining all types of MEs and 13–31% of residents in studies examining transfer-related MEs, and 75% of residents were prescribed at least one PIM. That said, serious effects of MEs were surprisingly low and were reported in only a small proportion of errors (0–1% of MEs), with death being rare. Whether MEs resulting in serious outcomes are truly infrequent, or are underreported because of the difficulty in ascertaining them, remains to be elucidated to assist in designing safer systems.

High risk medication administration in hospitalized elderly patients preceded falls

From a recent study:

Results

Of 328 falls, 62% occurred in individuals administered at least one high-risk medication within the 24 hours before the fall, with 16% of the falls involving individuals receiving two, and another 16% in individuals receiving three or more. High-risk medications were often administered at higher-than-recommended geriatric daily doses, in particular benzodiazepines and BRAs, for which the dose was higher than recommended in 29 of 51 cases (57%). Hospital EMR default doses were higher than recommended for 41% (12/29) of medications examined.

Conclusion

High-risk medications were administered to older fallers. Doses administered and EMR default doses were often higher than recommended. Decreasing EMR default doses for individuals aged 65 and older and warnings about the cumulative numbers of high-risk medications prescribed per person may be simple interventions that could decrease inpatient falls.

It would appear that EMR decision support contributed to the problem.

Medical error lunacy continues unabated

Green journal commentary, a must read.

Settled science: female physicians are better docs

So says one of the authors of the much talked about study in his somewhat, shall we say, promotional blog posts. [1] [2]