Is your #FallsPrevention Program Maximizing Inpatient Falls Risk Assessment?

Some patients will fall while they are in the hospital. How many? Bouldin and colleagues used National Database of Nursing Quality Indicators (NDNQI) data to find an overall fall rate in the U.S. of 3.53 falls per 1,000 patient days. The highest rates were in medical units, at 4.03 falls per 1,000 patient days. Overall, one fourth of the falls were associated with injury.(1) Collectively, the problem is large, and is estimated to impact 2% of all hospitalizations.(1)

Beginning in 2008, the Centers for Medicare and Medicaid Services (CMS) decided to stimulate U.S. hospital efforts to reduce inpatient falls by ending payments to hospitals for the additional costs associated with injury from inpatient falls.(2) As a result, fall prevention is one of the top priorities for most hospitals.

Nearly all fall prevention activities include the use of falls risk assessment tools. In the US, wristbands and bed signs are common warning labels to indicate patients identified as being at increased risk of falling through the use of these tools. But if risk identification is the main focus of your falls prevention program you are missing an opportunity to individualize fall prevention activities and reduce inpatient falls.


Fall risk assessments are not very good at risk prediction.

Does this surprise you? Several comprehensive systematic reviews have evaluated the performance of risk assessment tools for predicting an individual inpatient’s risk of falling.(3,4,5) None of the tools perform better than clinical judgment.

How can this be? You may remember from your epidemiology training that sensitivity and specificity respectively measure the proportion of fallers who tested positive with your tool and the proportion of non-fallers who tested negative with your tool. But did you remember other measures of the value of a screening or diagnostic tool? The positive predictive value is a better measure to evaluate the likelihood that a person who tests positive will have the condition. The negative predictive value measures the likelihood that a person who tests negative will not have the condition. Both measures are dependent on the validity of the tool and they are also dependent on the prevalence of the condition, which in this case is falls. How prevalent are falls in your organization?

Let’s look at some data presented in the NICE guideline to see how a tool with sensitivity and specificity of at least 70% (one of the inclusion criteria for studies for the NICE guideline) can have much lower predictive value.

For the Hendrich Fall Risk Model (data extracted for the NICE guideline from Hendrich et al 1995 as reported in the NICE guideline) (5)

There were 102 total patients who fell out of 338 patients. The test correctly labeled 79 of the 102 as falling, and 169 of the 236 as not falling

The sensitivity is 79/(79+23) = 77%.          The specificity is 169/(169+67) = 72%

But the positive predictive value is much lower. This measure looks at how many of the patients predicted to fall actually fell.

So the positive predictive value is 79/(79+67) = 54%.

Remember the predictive value is dependent on the condition prevalence, and even though falls are among the most common adverse events in the hospital, most inpatients do not fall.  

The negative predictive value, 169/(169 +23) = 88% is much better, but still misses the opportunity to identify and prevent falls in 12% of the people who were labeled low risk.


Using fall risk assessments to label patients as high or low fall risk misses the opportunity to individualize care. Should falls risk assessment be abandoned as a patient strategy? No, but the use of risk assessment as a screen to simply label patients should be abandoned. That use misses the opportunity to tailor interventions to patient needs to reduce the risk of falling during their hospital stay.

It’s not just that the quality of the evidence for inpatient risk prediction using any risk assessment tool is low or very low. Risk prediction produces a simple label that by itself does not guide staff action. After identifying a patient as being at increased risk, what is the next action step? Also, inpatient staff not regularly assigned to the patient, and other staff like lab, radiology and other technicians don’t know how to assist based on a simple warning alert. Patients, their family, and their friends may not have understanding of why the patient is labeled at risk nor what they can each do to change that risk. Risk predictions and resulting simple labels don’t provide actionable interventions. There is also potential for alert fatigue if too many patients are labeled with fall risk.  

There is moderate evidence from several recent systematic reviews that multi-factorial interventions can reduce inpatient falls, when multi-factorial is defined as interventions that are individually tailored to each patient’s modifiable risk factors. (6-11) How are the factors determined? Risk factors are identified through use of a risk assessment, whether tool or clinical judgment.

If risk assessment is used to identify why the patient is at risk of falling and then coupled with interventions directed to reduce those specific risks, it can be effective.  These systematic reviews and systematic overviews have concluded there is moderate evidence of the effectiveness of multi-factorial interventions. (6-11) Some of these review authors have lamented that the interventions vary so much from study to study that it is difficult to determine the essential elements. But that is just the point. The interventions must vary because falls are multi-factorial. Inpatient falls can be due to patient intrinsic factors like instability due to reductions in balance, strength, and agility or from loss of vision. They can arise from factors associated with hospitalization like unfamiliar surroundings, treatments and activity restrictions that add to confusion and instability.  And they can arise from combination of the above factors and additional factors like reduced bowel and bladder control leading to fear of toileting needs that can’t be met in the hospital environment.  To reduce preventable falls, interventions must be tailored to individual needs. The needs and plan of action must be communicated clearly to all staff who come in contact with the patient and with the patient and their loved ones so that everyone is empowered to reduce the opportunities for that patient to experience a fall.

Partners Health-Care System has tested this approach and successfully reduced falls from 4.18 to 3.15 per 1,000.  Dykes and colleagues used health information technology to incorporate risk assessment results (they used the Morse scale) with targeted intervention strategies. They developed specific signage and communication tools to use with patients and staff to clearly communicate the specific risks and the resulting actions to use to reduce falls. (12)

We have sufficient evidence from systematic review and from field-tested studies to stop using risk assessments for simple prediction and to start using them as the foundation to build individualized, multifactorial patient care plans. Is your organization using that evidence to build your fall prevention program?

TheEvidenceDoc 2015


1. Boudin ED, Andresen EM, Dunton NE et al. Falls among Adult Patients Hospitalized in the United States: Prevalence and Trends. J Patient Saf. 2013 March; 9(1): 13–17.

2. CMS Final Rule Federal Register August 19, 2008. accessed July 28, 2015.

3. Oliver D, Daly F, Martin /FC, McMurdo MET Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review. Age and Ageing 2004;33:122-130.

4. Haines TP, Hill K, Walsh W, Osborne R. Design-Related Bias in Hospital Fall Risk Screening Tool Predictive Accuracy Evaluations: Systematic Review and Meta-Analysis. J Gerontol 2007;62A:664-672.

5. National Institute for Health and Care Excellence June 2013 Assessment and Prevention of Falls in Older People Developed by the Centre for Clinical Practice at NICE accessed July 28, 2015

6. Cameron ID, Gillespie LD, Robertson MC, et. al. Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database of Systematic Reviews 2012, Issue 12. Art. No.: CD005465. DOI: 10.1002/14651858. CD005465.pub3.

7. DiBardino D, Cohen ER, Didwania A. Meta-analysis: multidisciplinary fall prevention strategies in the acute care inpatient population. J Hosp Med. 2012;7:497-503.

8. Coussement J, De Paepe L, Schwendimann R, et. al.. Interventions for preventing falls in acute- and chronic-care hospitals: a systematic review and meta-analysis. J Am Geriatr Soc. 2008;56:29-36.

9. Oliver D, Connelly JB, Victor CR, et. al. Strategies to prevent falls and fractures in hospitals and care homes and effect of cognitive impairment: systematic review and meta-analyses. BMJ. 2007;334:82.

10. Shekelle PG, Wachter RM, Pronovost PJ, et. al. Making Health Care Safer II: An Updated Critical Analysis of the Evidence for Patient Safety Practices. Comparative Effectiveness Review No. 211. (Prepared by the Southern California-RAND Evidence-based Practice Center under Contract No. 290-2007-10062-I.) AHRQ Publication No. 13-E001-EF. Rockville, MD: Agency for Healthcare Research and Quality. March 2013. accessed July 28, 2015.

11. Miake-Lye IM, Hempel S, Ganz DA, Shekelle PG. Inpatient Fall Prevention Programs as a Patient Safety Strategy A Systematic Review. Ann Intern Med. 2013;158:390-396.

12. Dykes PC, Carroll DL, Hurley A et al. Fall Prevention in Acute Care Hospitals: A Randomized Trial. JAMA 2010;304:1912-1918.


#PSW2015 - Patient Safety Week, Enthusiasm, and New Projects

It's Patient Safety Week and it's great to see the passion, enthusiasm and sharing this week. Are you and your organization eager to try out some new projects you learned during the latest conferences and webinars? Wonderful but....WAIT!

What do I mean wait? Why shouldn't you rush to introduce these new interventions while the excitement and enthusiasm is high? This may surprise you, but on the basis of chance alone, there is more potential for change to introduce harm than good. HOW?

You have the opportunity to increase benefit, decrease benefit, or get no change in benefit to patients or staff from implementing an intervention. Also remember, an intervention may increase harm, decrease harm, or have no effect on the amount of harm to patients or staff. By chance alone, any new intervention could create the following options:

Effect on Harm          Effect on Benefit                 Overall Result

Decrease Harm           Increase Benefit                   WIN/WIN

No change in Harm    Increase Benefit                   WIN

Increase Harm            Increase Benefit                   Can you manage the harm?

Decrease Harm           No change in Benefit           Value dependent

No change in Harm    No change in Benefit           LOSS - What a waste

Increase Harm            No change in Benefit           LOSS

Decrease Harm           Decrease Benefit                  Value dependent

No change in Harm    Decrease Benefit                  LOSS

Increase Harm            Decrease Benefit                  MAJOR LOSS

You have a 1 in 9 chance of increasing benefit and decreasing harm, your preferred outcome. You have the exact same chance, 1 in 9 of getting a major loss by increasing harm and reducing benefit. But you also have a 4 in 9 overall chance of outcomes you do not want, and 3 in 9 of outcomes that may or may not be a net improvement for your organization. By chance alone, only 2 out of 9 are definite improvements.

How can you improve your odds? To get the odds in your favor, you need evidence. You can use evidence-based methods to choose practices likely to have better chance of success in your organization. See TheEvidenceDoc checklist for more detail. Things to consider are to evaluate whether or not the innovation actually worked where it was tested - Were the outcomes measured in the same way before and after implementation, for example? If it makes a clinically important (not just statistically significant difference), could it work for you? The project leaders need to provide enough detail about the setting, the patient population, the intervention and the outcomes for you to evaluate how well the innovation might translate to other organizations. And what are the harms? Could you make it safe to try the project in your organization?

Learn to use evidence to increase your chances for a success!





Azithromycin and arrythmia - another reason to Choose Wisely

Today the FDA released a Safety Announcement to warn the public and health professionals that azithromycin can cause heart abnormalities leading to sudden death.  Azithromycin is a macrolide antibiotic, a type of antibiotic already known to be associated with abnormal heart rhythm and an increased risk of sudden death. But azithromycin was widely believed to have minimal toxicity to the heart, because prior studies had not detected the potentially lethal effect. It was that belief that lead it to become widely prescribed, often in elderly patients with heart disease who may be more susceptible to the drug's cardiac effects.

In 2009 JAMA published a study of antibiotic use for acute respiratory tract infections (colds) with Big Data from a national database. The study had encouraging findings of an overall decrease in the use of antibiotics for colds - most caused by viruses which are not treatable with antibiotics. But it also found that even though most antibiotic use decreased, azithromycin use increased substantially, six times between 1995 and 2006. This data shouldn't surprise us. The Z-pack has become so common that patients ask for it by name.

The FDA summary of the data that prompted the warning comes from a NEJM study published in May of 2012 and a manufacturer study that found azithromycin is associated with QT interval prolongation - an irregularity of the heart rhythm that can lead to sudden death.

The NEJM study was a very well designed and conducted large study in a very large population with relatively complete medical record.  The NEJM study is a good example of the use of Big Data to examine a patient safety issue. The study authors used a statewide database to evaluate the risk after published case reports suggested it. But it was an observational study, a design often disparaged outside the research community. However, well done observational studies are often our best chance at finding harm associated with therapy because, thankfully, harms are relatively rare. Randomized Controlled Trials (RCTs) are often too small or too short in follow-up time to detect increases in harmful side effects.  Large observational studies that are carefully planned and carried out to reduce the risks associated with non-experimental design can find these increases.

So we have data that the Z-pac is not as safe as once believed. Data to consider when balancing the benefit and risk to an individual patient. Even when side effects are rare, as they are in this instance, we need to evaluate the seriousness of the side effect, in this case sudden cardiac death.  We must balance that risk against the potential benefit from using the drug and assess whether or not the patient needs this specific antibiotic or even any antibiotic. 

We have data that while overall antibiotic prescribing for colds is down, prescribing for azithromycin is up, way up. It kills more bacteria and it's easier for patients to complete a full course of therapy.  Patients ask for it by name.

And we are concerned about antibiotic resistant bacteria. 

Do we need more evidence to Choose Wisely when considering a prescription for antibiotic therapy?  The FDA is not saying we should stop using azithromycin. The FDA is warning that our prior belief that it did not share the cardiac toxicity of others in its class was wrong. Dead wrong.

It's time to consider this drug choice more carefully and choose wisely when evaluating antibiotic therapy. Does this patient need azithromycin? Would amoxicillin be effective? Does this patient need an antibiotic at all?


Postscript -Dr. Ireland commented on this issue last year, shortly after the release of the study and the FDA statement.