Who is driving #populationhealth

What is population health management anyway? The short answer is, it depends on who you ask. There are many players in the population health space right now and their differing approaches and agendas are driving the confusion.

Why the sudden interest? In the US, healthcare is beginning a gradual change from its current focus on treating a single disease to helping whole people maximize their state of health and well being, and ultimately to helping whole groups of people be their healthiest. To do that we must improve the health status of people across the entire health spectrum, from preventing disease, through early disease detection, to managing the consequences of disease.

To help you understand the many drivers of interest in population health management, I created this diagram.

This diagram isn’t meant to add to your confusion but to explain some of the cause of the confusion. Let’s start on the right side of this slide, and I’ll describe each of the drivers and a little of their role.

So the many different drivers with their different goals is the reason you hear so many different definitions and buzzwords associated with population health management. These represent different pieces of the population health management puzzle. And organizations are choosing to focus on different parts of the puzzle as they approach population health management.


If you’d like to learn more about these drivers, you can download a free copy of the chart of Drivers of Interest in Population Health with links to the driver organizations and some of their resources.

TheEvidenceDoc 2016


#Wholecare for #PopulationHealth Management

If you are in healthcare, you are aware that population health management is a very popular buzzword right now.  Search for videos on Population Health Management on You Tube and you’ll find 38,000 to watch; search for #populationhealth on Twitter and you’ll have plenty to read.

So the phrase is hot. But what does it mean? Do you know? I share a guest blog on the Primaris website next week that will explain some of the source of confusion.

Why does population health management seem so confusing? The short answer is that there are many drivers of interest with different short term goals.
— TheEvidenceDoc

With different drivers and goals, you will hear different terms and buzzwords - words like:

  • prevention
  • risk prediction
  • risk management
  • disease management
  • care coordination

These buzzwords represent different pieces of the population health management puzzle.

But instead of examining the pieces of population health management, let’s look at the whole. In the US the change in focus to population health will be a gradual change in how we will plan for and deliver care. Some will say this is a radical change.

This change is from our current care focus on treating a single disease to helping whole people maximize their state of health and well being, and to helping whole groups of people to be their healthiest.

This change is from our current care focus on treating a single disease to helping whole people maximize their state of health and well being, and to helping whole groups of people to be their healthiest.

We’ll move from treating the diabetic in room 11, to helping Mrs. Smith be her healthiest, to ultimately improving the health status of all the citizens of Healthy Town, USA or however we define our service reach or population.

This movement will require a new approach in healthcare delivery, because caring for whole patients and whole populations requires whole care.
— TheEvidenceDoc

Whole care is more than just treating one disease at a time. And it’s also more than coordinating care for a patient with multi-morbidities. It’s about providing complete care through the spectrum of health.

In public health, we’ve traditionally divided the stages into:

o   preventing disease before it starts

o   screening to find early signs of disease before the person is aware they have disease as well as prompt diagnosis when symptoms do appear

o   and finally, to provide care that treats as well as manages the complications of disease.

These categories of primary, secondary, and tertiary prevention cover the spectrum of care.

Primary prevention has been somewhat neglected in clinical settings. The most common primary prevention activity in clinics is the delivery of immunizations. There are other primary prevention activities people can engage in like exercise and healthy diet, but social actions like education and jobs and sanitation also help people stay well.

Secondary prevention is the early detection of disease to intervene before it has an impact on a person’s life. These activities are often labeled as prevention in the health care system, activities like mammography and colonoscopy. But because they don’t prevent the disease, just find it early, they are technically secondary forms of prevention. Their goal is to find the disease early enough to prevent the pain and suffering of advanced disease and to sometimes offer cure.

Tertiary prevention is where healthcare in the US has focused and excelled. It seeks to delay the progression of disease and manage its consequences.

The whole care of population health management will cover this whole spectrum, keeping people as healthy as they can be and minimizing the consequences of any disease they develop.



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. http://www.gpo.gov/fdsys/pkg/FR-2008-08-19/html/E8-17914.htm 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 guidance.nice.org.uk/CG161 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. www.ahrq.gov/research/findings/evidence-based-reports/ptsafetyuptp.html 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.