Productivity Paradox and EHR

Bob Wachter and Michael Howell are optimistic about the future improvements in health information technology and its impact on physicians. I share their enthusiasm and I am optimistic about an increase in productivity in healthcare sector. And why not? Its about time.

Productivity in every other sector has resulted in decreasing utilization of human resources and increasing production of goods. However, such is not the case in healthcare. A physicians still sees about the same number of patients in a day as he/she used to see 50 or 100 years ago. It is quite possible that the number patients seen might have dropped due to documentation and other requirements. The result is evident; healthcare sector now accounts for almost one-fifth of the GDP in the US. Much of this has to so with the lack of growth in productivity in healthcare sector as compared to other sectors.

What is promising is that artificial intelligence will likely to increase productivity in the healthcare. I don’t think that this will come through improvements in health information technology (HIT). I believe it will come with better utilization of HIT as data repository for learning algorithms. Technology will also help in data capture, and it does so in many ways even now, but most importantly in data inference. However, it will not be good for an average physician.

Remember, productivity can increase only if more patients can be seen by fewer physicians (or healthcare workers). That is not possible with one physician simultaneously seeing several patients. It is possible only when machines see most patients and physician sees patients only in those situations where algorithms have not gotten enough data to develop a routine.

And what those situations will occur in relatively rare conditions. These will not our usual common diseases, what we might call ‘bread-and-butter’ medicine. Rather these will be either rare diseases or rare manifestations of common diseases. A physician overseeing such a highly productive healthcare delivery system will have to be a master clinician and a superb diagnostician.

In other words, an increase in productivity in healthcare sector will spell the doom for majority of physicians ….. happy to hear what you think……….

Hospital Readmissions and Malnutrition

While it is easy to imagine how malnutrition can be related to excess readmissions, there are not very many studies examining this association. Of note, the CMS adjustment algorithm for expected readmissions takes into account malnutrition.

This retrospective study from Australia, examined this association among 26 253 admissions, representing 19 924 patients with a 6-month readmission rate of 44.8%. They found that malnourished patients had 40% higher risk of readmission within first week after discharge and 23% higher risk of readmission between the second week and 23rd week after discharge. Authors concluded that malnutrition was a strong predictor of unplanned readmissions and suggested examination of targeted nutritional interventions to lower readmission rate. 

Artificial Intelligence versus Human Doctors

While it is relatively easy to imagine artificial intelligence taking over pattern recognition type of physician work (such as reading images like radiologists or identify skin conditions by looking at the skin like dermatologists), the job of a general internist is much more demanding and requires assimilation of data inputs from a large number of sources, hence making it difficult for an artificial intelligence system to accurately predict a disease (or prepare a list of differential diagnoses).

On the other hand, the process is not, and should not be, very difficult for a well-crafted system. Razzaki et al tested such an artificial intelligence algorithm and compared it with physicians in its ability to predict diagnosis. For their study, they adopted a semi-naturalistic, role-play paradigm that simulated a realistic consultation between a patient and either their artificial intelligence system or a human doctor. Their study was designed to assess both the clinical (diagnostic and triage) accuracy and the ability to gather all of the relevant history from the patient. What they found was that their artificial intelligence algorithm was able to provide diagnostic and triage advice with a level of accuracy and safety approaching that of human doctors. If there study results are replicated then such systems may not only reduce costs and improve access to healthcare worldwide but may also provide better standardization of care delivered to patients.

Obesity and Increased Risk of Death

It is now well-known that obesity (BMI>29.9) or weight below the normal range (BMI<18.5) are associated with high risk of mortality. On the other hand, just being a little overweight (between 25 and 29.9) may reduce overall risk of death. These results were mainly based on a study published in 2005. However, there were some questions about possible bias due to incomplete adjustments for some risk factors such as smoking.

An update of these results with analysis for potential bias was recently published and available on NHANES website. Basically, the results remained the same and obesity and underweight both groups remained associated with high risk of death.

Of particular interest is the underweight group; while lay media has been focused on obesity, the other extreme, that is being underweight, is also associated with increased risk of death. In fact, the risk of death may be twice as high as normal weight in underweight individuals between the ages of 25 and 59 years.

Nutrition Data in NHANES

Ahluwalia et al published a review article (now updated) that gives a very interesting and comprehensive overview of dietary data collected in NHANES. The data is available publicly and dataset is really large allowing for examination of a large number of hypotheses.

Geographic Rounding and Patient Satisfaction

Geographic rounding is often touted as a way by which healthcare team can communicate better among themselves and thus patient will get similar messages from all team members. Further, team members will appear to patient have strong team-work . Lastly, this will also allow healthcare team to spend more time with patients. Hence, patient satisfaction will increase. While it sounds reasonable in theory, there is little evidence supporting this logical sequence. Siddiqui et al examined this question in their recent study published in the Journal of Patient Experience.

Interestingly, they found that the patients cared for by geographically localized teams did not have better patient experience. They suggest that other factors such as physician communication skills may overshadow the impact of having localized teams. Obviously, further research is needed to better understand organizational, team, and individual factors impacting patient experience.

Phytoestrogens and Liver

Genistein is a phytoestrogen with similarities to female sex hormones and, has been shown to prevent nonalcoholic fatty liver injury in animal studies but human studies are lacking.

In this study, authors, analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2010 and examined the relationship between normalized urinary genistein (nUG) and serum ALT using linear regression models. Investigators further examined the differential effect of sex using an interaction term.

In almost 10K patients, authors found a statistically significant association between genistein and liver function in males, but not in females. At least for me, the sex-specific role of genistein in mitigating liver disease is very interesting.

Sepsis and qSOFA, SIRS, NEWS

Sepsis is a rather common admitting diagnosis to hospital with high mortality rate. Identifying patients who are at a higher risk of adverse outcomes may help to optimize resource allocation. To this end, initially SIRS definition was recommended. More recently, qSOFA is being promoted as a predictor of adverse events during hospitalization due to sepsis. However, there is a lesser known NEWS score that is also being used at few hospitals.
This study compared the three scores in a retrospective sample and found that NEWS may be similar or superior to qSOFA. Note that qSOFA (3 variables) is the quickest and simplest of the three while NEWS is the most complex (20 variables).

A Fantastic Weekly Update on Artificial Intelligence

I have been enjoying this weekly update by Azeem Azhar. And I invite you to try it too. Here is the link

http://www.exponentialview.co

SES Adjustment for Readmission Penalty

CMS started implementing the Hospital Readmissions Reduction Program (HRRP) on October 1, 2013, penalizing hospitals with higher readmission rates. While readmission rates were adjusted for several factors, socioeconomic status (SES) was missing leading to protests and lobbying by hospitals caring for poor people. Hence, in 2016 the US Congress directed the CMS to to adjust for low SES. In response, CMS has made following adjustments to HRRP.

1. Make five peer groups of hospitals based on SES. SES will be determined by the proportion of “dual eligible” Medicare beneficiaries (those who are also eligible for full Medicaid benefits).
2. Calculate the median excess readmission ratios (ERRs) within each peer group to adjust for penalties
3. Scale penalties using the budget neutrality factor.

Fuller and colleagues analyzed the effect of SES adjustments (as outlined above) and examined the effect on the number and distribution of penalized hospitals. An interesting read!

Protection of Gut Microbiome from Antibiotics

While antibiotics are essential for treatment of bacterial infection, an increasingly recognized adverse effect of antibiotic use is alteration in gut microbiome. The altered gut microbiome can result in several acute and long-term unhealthy effects. This small clinical trial tests the use of an adsorbent to to mitigate the effects of antibiotics on gut microbiome. Investigators found, using the shotgun quantitative metagenomics, that the richness and composition of the intestinal microbiota were largely preserved in subjects using antibiotics when co-treated with DAV132 (the adsorbent used in this study). This is an important study, and if the adsorbent is effective without compromising the antibiotic availability in blood, may be extremely useful in clinical medicine. 

Geographic Latitude, Blood Pressure, and Serum Cholesterol

This is a provocative study pointing out the association of geographic latitude with serum cholesterol levels. Obviously such studies, at country level, are fraught with biases including ecological fallacy. However, the underlying etiology of the observed differences need to be explored further.

Designing a RCT and Network Meta-analysis

A review of literature with possible meta-analysis of available data is often recommended before a randomized clinical trial (RCT) can be proposed. This article goes a step further and suggests using network meta-analysis for planning and designing a RCT. Authors emphasize that a trial design based on updating the evidence from a network meta-analysis of relevant previous trials may require a considerably smaller sample size to reach the same conclusion compared with a trial designed and analyzed in isolation.

Weak vs. Strong Social Ties

The relative contribution of the number of strong social ties versus the number of weak social ties to the health status was explored in this study. Authors examined social network characteristics as predictors of mortality in the Finnish Public Sector Study (n = 7,617) and the Health and Social Support Study (n = 20,816). At baseline, social network characteristics were surveyed. During a mean follow-up period of 16 years, participants with a small social network (≤10 members) were more likely to die than those with a large social network (≥21 members) (adjusted hazard ratio (HR) = 1.23, 95% confidence interval (CI): 1.04, 1.46). Mortality risk was increased among participants with both a small number of strong ties (≤2 members) and a small number of weak ties (≤5 members) (HR = 1.55, 95% CI: 1.26, 1.79) and among participants with both a large number of strong ties and a small number of weak ties (HR = 1.28, 95% CI: 1.08, 1.52), but not among those with a small number of strong ties and a large number of weak ties (HR = 1.04, 95% CI: 0.87, 1.25). Authors conclude that the number of weak ties may be an important component of social networks for mortality risk.

Diagnostics After Multilevel Regression in Stata

Here are few diagnostics that can be run in Stata after running a multilevel model

1. Assess normality of residuals

predict resid_std, rstandard
qnorm resid_std

2. Examine relationship between predicted and residual values

predict resid, res
predict fit, fit

3. Examine variance-covariance matrix

estat recovariance
estat recovariance, correlation

4. Determine Cook’s D and DFBETAs using MLT module

net install mlt.pkg    /* Installs MLT package*/
mltcooksd              /* reports Cook’s D of the whole model */
mltcooksd, fixed       /* reports Cook’s D of the Fixed part */
mltcooksd, random      /* reports Cook’s D of the Random part */
mltcooksd, approx      /* Uses approximation and returns results faster */

A very easy and simple video explaining Bayes Theorem

Must watch TED talk

Talks about how artificial intelligence algorithms are able to affect our opinions. We all are vulnerable, yes we ALL are.

Case against propensity scores

Here is an interesting video (couple of years old but still relevant)

CLABSI Trends and Parenteral Nutrition

Healthcare-associated infections (HAIs) harmful for patients and costly for the health system. Central line associated bloodstream infections (CLABSI) are the most costly of HAIs (between 45K to 55K per CLABSI), increase length of stay by several days, and increase mortality by 15% to 40%. Although the rate of CLABSI has been decreasing since the institution of several preventive practices, the rates remain pretty high.

Patients who receive parenteral nutrition have more than 4-times higher risk of CLABSI than those who who do not receive parenteral nutrition. However the data on the risk of CLABSI risk since the institution of penalties for CLABSI by CMS (Centers for Medicare & Medicaid Services) was not available, that was until the publication of study by Fonseca et al. They used data from all adult patient discharges between January 1, 2009, and December 31, 2014, from 2 affiliated hospitals in a large health system in New York City. They conducted univariate and multivariate analyses to examine the relationship and temporal trends between parenteral nutrition and CLABSIs.

Of the 38,674 patients with central lines, 3517 developed CLABSIs. Of these 3517 patients, 767 patients were prescribed parenteral nutrition. Patients who were prescribed parenteral nutrition were 2.65 times more likely to have CLABSI than patients without parenteral nutrition. What this study shows is that there has been a decrease in the risk of CLABSI among patients who receive parenteral nutrition, although the risk remains much higher. Obviously, this study advocates for additional research to identifies strategies to decrease the risk of CLABSI in patients who receive parenteral nutrition.

Burnout

Burnout among healthcare workers, particularly physicians, has gained increasing attention recently. The societal expectation is that physicians will be selfless and put their patient’s needs first. Often physicians are expected to work long hours and do whatever it takes to help their patient and to go the extra mile; in other words give one's all. Burnout is further exacerbated by the changes in national health system and healthcare organizations; such changes are resulting in work environments that are high in demands and low in resources.

However, what is burnout is open to interpretation. Experts still debate about the dimensions of burnout. The most common burnout measurement tool, Maslach Burnout Inventory (MBI) assumes three dimensions of burnout; emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). MBI has been criticized for its various aspects. For example, it measures three dimensions of burnout (EE, DP, PA) but recommends against merging those three dimensions to reach to the measurement of burnout itself. Thus, MBI is measuring three concepts but unable to define a single concept of burnout. Another criticism is that burnout is an amalgam of an individual state (EE), an undesirable coping strategy (DP), and result of the EE state (lack of PA). However, the biggest criticism of MBI is that it is not available in public domain.

The dimensions of burnout are open for discussion. While MBI, as noted above, considers that burnout has three dimensions, others consider burnout to have two or even one dimension; it is possible that burnout may have more than 3 dimensions. For example, Oldenburg Burnout Inventory considers only two dimensions of burnout out while Copenhagen Burnout Inventory (CBI) considers only one dimension of burnout.

Obviously, the disagreements about the definition and dimensions of burnout limit the study of effective interventions and have lead some to suggest that burnout perhaps does not exist as a separate entity on its own.