The Renaming of an Illness: “Dementia” vs. “Major Neurocognitive Disorder” – Five Years Out

About five years ago, I noted a big event – after years of debate, the “big APA” folks (e.g., the American Psychiatric Association) had proposed that “dementia” be from heretoforward renamed as “major neurocognitive disorder.” In the article, I noted that there were some good reasons for proposing this change. For one thing, “dementia,” like “senility,” has some negative etymological baggage:

“the origin of the word ‘dementia’…. (is) a bit harsh as well, from the perspective of those who carry the diagnosis… ‘dementia’ originates from the latin term demens literally ‘mad, raving.’”

So, five years out – has this changing of the terminology cause any big changes in the field? It’s worthwhile looking at exactly why the “big APA” decided to make this this change in the first place.

From the DSM-5 online:

“Dementia is subsumed under the newly named entity major neurocognitive disorder, although the term dementia is not precluded from use in the etiological subtypes in which that term is standard….. The term dementia is retained in DSM-5 for continuity and may be used in settings where physicians and patients are accustomed to this term. Although dementia is the customary term for disorders like the degenerative dementias that usually affect older adults, the term neurocognitive disorder is widely used and often preferred for conditions affecting younger individuals, such as impairment secondary to traumatic brain injury or HIV infection.”

So, what it appears the “big APA” folks are saying is that they want us to stop using “dementia” when we’re talking about some disease entities where the term may be in less “standard” use – but if practitioners wish to keep using the term dementia as they always have, well, they can.

Where may the term be in less “standard use”? Well, given the association of the word “dementia” with older adults, nursing homes, and the aged, it seems pretty fair to say that “dementia” means “old people” to most. In practice, it’s worth noting that in my tiny corner of the geropsychology world, “dementia” continues to be the standard term. This may be for a number of reasons: for one thing, “major neurocognitive disorder” is well, rather wordy. Also, family members and consumers are not aware of the term. They typically have heard of “Alzheimer’s,” and may be aware of the term “dementia” (and at times, often ask “is dementia different than Alzheimer’s”)?

Moreover, it’s not particularly clear that the term “major neurocognitive disorder” has quite seeped into the public consciousness yet. Out of curiousity, I did a quick “Google Trends” dive, looking to compare the terms “major neurocognitive disorder” and “dementia.” Suffice it to say – while searches for the term “major neurocognitive disorder” are on the rise, it seems pretty clear the latter term has gained comparatively zero traction.

Searches for the term “dementia” appear to have over forty-plus times the
average volume, and continue to rise year upon year. It’s worth noting the same is true for searches within Google Scholar – which is a pretty good proxy for search use amongst academics and clinicians – a search for articles between the years 2015-2019 for the term “neurocognitive disorder” yielded just under eight thousand hits. “Dementia,” in contrast, yielded over a quarter million. Clearly, dementia wins.

This probably reflects a few things. One – the term “dementia” is entrenched as the “standard” term. Two, it probably reflects that the clinical wordiness of the new moniker is too unwieldy for consumers, and probably for clinicians and academics as well.
Would the world of dementia care be different if somehow we all got on board and dropped the use of the term “dementia” outright? Would this translate into more humane and person-centered care for persons with dementia / major neurocognitive disorder? At this point, we don’t know, because five years out from the APA’s grand semantic shift, “dementia” continues to rule the diagnostic roost.

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Artificial Intelligence, Machine Learning, and Long Term Care

I’ve been wanting to write this article for a long time. I’ve been working in geropsychology and the long-term care (LTC) industry for the last 14+ years. I’ve worked mostly in the area of skilled nursing (what most people know of as nursing homes), as a consultant and staff member. Skilled nursing facilities are where people, typically OA (OA), live and receive medically necessary professional services from nurses and other allied health professionals to provide them assistance with activities of daily living.

Over this time, I’ve learned a few things. Skilled nursing care, at least as it’s practiced in the United States (but this is largely true across the industrialized world) is highly regulated, very labor intensive, and because of these two reasons – is very expensive. Caring for medically complex and frail, chronically ill, primarily older adults (OA) involves a lot of monitoring and supervision, along with the physically demanding tasks of caring for their bodily needs.

Back to the issue of cost – according to recent statistics, the average monthly cost of a stay in long-term care in the United States is over eight thousand dollars per month ($8,121 to be exact). While that number is obviously high, it’s sobering to recognize that the cost is 13% higher than it was just 5 years ago (citation here).

Setting the stage – Recapping the “Demographic Tsunami”

And these trends aren’t going to be slowing down anytime soon. As I’ve written here, and here, and a number of other places, the US and the rest of the industrialized world is currently poised to be swept away by a “demographic tsunami.” This refers to the fact that from the year 2000 to 2050, the world’s population who is 60 years or older will approximately double from about 12% to 22%. The number of “oldest old” (those over age 80), will quadruple.

Moreover, there’s strong data suggesting that there are, and will continue to be growing and severe shortage of healthcare professionals out there to meet the needs of OA (both in long term care and otherwise). In my own particular field, geropsychology, the shortages are already severe and are projected to continue to grow. The story is not much different for the fields of geriatric medicine or geriatric nursing – even the front-lines of LTC, the people who do the real demanding, physically laborious work of caring for elders, the nursing assistants – even this field is experiencing growing shortages.

There’s a number of reasons why this is. One, reimbursement. Geriatric medicine is a field with one of the lowest reimbursement rates of any medical specialty, so, in response, it’s a very poorly sought-out specialty for the hordes of newly-minted physicians who want nothing more than to pay off their increasingly-herculean student loans and to start providing a good living to their families (and who can fault them for that?). Geriatric nursing isn’t much different, and in the case of nursing assistants, hourly wages of 10$ per hour in very demanding conditions provides a poor retention incentive.

So why is this such a problem?

So far, it’s fair to say that the rapidly growing cost of nursing home care and elder homecare and facility care in the United States and industrialized countries is not stopping. While our current generation of 65-year-olds and older are (arguably) the healthiest they’ve been in a long time, the fact is – when people get old, they are at far greater risk of developing a whole host of problems that often require significant and at times, round-the-clock care. For example:

  • Dementia – I never fail to mention this one. The #1 risk factor for developing dementia (such as Alzheimer’s disease), is advanced age – and, dementia is endemic in LTC facilities (at the VA nursing home I am employed at in my day job, around 70% of my population have dementia, which is roughly in line with US averages).
  • Falls and ambulation problems – chronically ill OA are more likely to lose the ability to walk as they get older. Musculoskeletal issues (like degenerative joint disease), deconditioning and muscle wasting, dementia, and other issues can often render OA wheelchair bound or worse. Sometimes they are unable to transfer from wheelchair to bed or toilet without assistance, or require lifts to be moved. Sometimes they are even unable to turn themselves in bed. Also – closely related to the issue of ambulation is falls – older people are much more prone to injuring themselves during a fall, and of falling more frequently. This is due to issues like cognitive impairment and poor judgment (due to the aforementioned issues with dementia).
  • Incontinence – another major issue is lack of control of one’s bowels or bladder. Again, medical conditions like dementia, spinal cord injury, or other neurological problems can put chronically ill OA at risk for this. Obviously, incontinence, when combined with other issues, can require care as incontinence briefs need to be regularly changed so as to prevent other issues.
  • Difficult-to-heal, or nonhealing wounds – often the above issues of problems with ambulation, incontinence, or frequent falls often put chronically ill OA at risk for developing wounds that are often very difficult to heal. Often this is because they are less likely to move when they are in bed or in a chair (leading to pressure sores), or they can bang or scrape themselves during a fall. Due to thin skin and reduced ability to heal (often because of poor circulation, diabetes, etc.), their wounds take a very long time to heal, and without constant care, can at times become infected.

And this isn’t even scratching the surface. As you can see, chronically ill OA require significant amounts of monitoring and care by professionals in order to just exist, and without it, they can rapidly become acutely ill and require much more expensive care (such as in an emergency room).

So what’s the solution?

There’s been much discussion about solutions over the years, from making OA healthier, to strengthening home and family caregiving options, to training more doctors, nurses, and psychologists to help OA living in nursing homes, as well as improving existing models of care.

It’s also worth mentioning that with all the hullabaloo of the so-called “Affordable Care Act,” AKA Obamacare, there has been virtually no attention paid to reforming the broken state of LTC funding in the United States (I’ll quickly get off that soapbox!).

So what if there was some other solution? We all know, as is was said here by venture capital investor Shourjya Sanyai, that the “rapid(ly) aging demographic will directly affect social, economic and health outcomes for these growing economies. Particularly healthcare delivery pathways need to be readjusted, keeping in mind the prevalence of chronic diseases, comorbidities and polypharmacy requirements of the elderly and geriatric patients.

Sanyai goes on: “Given the situation, healthcare providers are starting to offload certain parts of the care-pathways to artificial intelligence (AI) based automatization. AI can now be found in every step of the care-pathway, starting from intelligent tracking of biometric information to early diagnosis of diseases.
So what is AI?

Artificial Intelligence and Machine Learning – Definitions, and the Example of Alexa

The definition of artificial intelligence, as found via Google: “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” Note that artificial intelligence (or AI, as it’s frequently called) is frequently combined with smart machine learning algorithms – which thereby assure a system that can do specialized cognitive tasks and also learn from experience as it does it’s work.

Moreover, one of the nice things about today is that AI and machine learning is a concept that’s rapidly seeping into the consciousness of consumers worldwide:

echo picture

Figure 1. Amazon Echo (http://www.bestaiassistant.com/google-home/amazon-echo-vs-google).

Amazon probably has done more to make the concept of AI (and it’s capabilities) obvious to the masses with the introduction of the Amazon Echo device, which is the most well-known vehicle for its so-called Alexa AI technology. What does the Echo do? One of the things we like most about the Echo in our family (we have several) is that it takes rote tasks and automates them.

For example, we have most of the lightbulbs in our house now controlled by Alexa – instead of having to get up and flip a switch, we say “Echo, turn off bedroom lights,” etc. We’ve also hooked it to our music streaming service, so if we want to hear a song, we say “Echo, play (insert favorite song),” and off it goes – this is as opposed to fiddling with our phones, or a CD player, or whatnot.

There’s more – Alexa will now be doing double-duty as a burglar alarm! One of the newer “skills” that Alexa has been enabled with is something called “Guard Mode” – whereby if a user leaves their home, they can say “Alexa, I’m leaving,” and Alexa will listen for the sound of glass breaking and alert the user. So – Alexa is also a smart monitoring system – while you can still purchase an analog burglar alarm system monitored by “24/7 security personnel” (which is expensive and requires people on duty to constantly monitor your home), you don’t have to – because AI (in the form of Alexa), will do it for you.

AI and Machine Learning in Healthcare

So let’s go full-circle back to LTC. Why would be interested in it?

First, remember all the examples I listed above, regarding the kinds of problems regularly addressed by nursing staff in LTC facilities (e.g., dementia, falls, wounds, incontinence, ambulation and movement issues). What they all have in common is that in order to address them in the LTC environment, they require a significant amount of personnel to perform rote tasks relating to monitoring and rounding. I would suggest that a very significant number of these tasks currently performed by nursing staff could be offloaded to so-called “smart systems.”

Smart Device Assisted Living and Monitoring. What happens when an older adult with dementia tries to leave their nursing home (trying to “go home”)? The current standard approach to “wandering” is to attach “wander guards” to residents at risk, which, when residents cross a perimeter, will alert staff and allow them to redirect them back into the facility. The downside of this is that often these alarms don’t help to locate residents (it just alerts them that the perimeter has been breached), nor does it distinguish between those who legitimately are trying to escape, versus those who merely accidentally trip the alarm when they are merely, say, rolling outside to get a breath of fresh air.

Or how about when an older adult is bedbound, is at high risk for nonhealing wounds, but due to neurological impairment fails to turn themselves? The current standard of care is for nursing staff to regularly “turn” residents (say, on an hourly or per-shift basis). However, this requires that nursing staff, who are often busy, overworked, tired (and human) to remember to remember – moreover, it’s often possible that these residents may be still turning on their own and don’t need this extra intervention.

AI is tailor-made for the situation I sketched out above, in the case of “turning” residents. You can apply a wrist-mounted (or bed-mounted) sensor to a resident and monitor their movements. If a resident has not turned after a certain amount of time, you can have the AI system alert staff proactively, essentially telling them, “hey, Mr. Jones hasn’t turned in a while – can you go help him?” This prevents staff from having to round unnecessarily on residents who are not at risk for wounds and who are turning in bed, and also relieves nursing staff from having to “remember to remember.” One of the companies mentioned in this article are already developing an AI system to address this very issue.

Wrist-actuated actigraphy (such as what you see with Fitbits, and Apple Watches), combined with AI, is also potentially ideal for replacing the old “wanderguard” system – I am familiar with a company called Carepredict, who is essentially doing just this – they have facility residents wear their own proprietary wristbands which detect a resident’s movements within the unit (as well as level of activity). The system is designed to provide “early warnings” to staff when a resident’s behaviors deviate from their established norms – and can precisely locate a resident when they are trying to escape (this is also something AICare is also trying to do).

Not only that, Carepredict claims they can provide “early warning” for staff to let them know if a resident is becoming depressed (say, if they begin to isolate in their rooms when their previous pattern was to be out and about regularly), or if they have stopped eating.

Fall Detection and Prevention. How are falls currently addressed by nursing staff?

Alimed Pressure pad

Figure 2. Bed / chair alarm pressure pad (courtesy of Alimed, Inc).

Currently, it’s the ol’ analog pressure-pad system. In other words, residents identified as being high fall risk are issued pressure pads placed on their beds or chairs, and if a resident gets up from their bed or chair, the alarm sets off a loud racket, and nursing staff come running. The downside of the current system are manifold – one, it has a significant number of false positives – residents who merely move in their chair or bed (something we *want* them to do, actually) set the alarms off. Second, the noise is annoying to residents, and for those with dementia, can serve to agitate them further – thereby inadvertently raising their fall risk. Third – it leads to “alarm fatigue” in staff (due to the frequent false positives) – staff sometimes don’t respond to the alarms because they know they are often wrong. Despite all of this, residents continue to fall at high rates, staff often find a resident on the floor and are left to question these frequently-memory-impaired residents and otherwise piece together what happened, and then institute fall prevention measures after the fact.

Enter Safely-You, a company I’ve been very excited about (although note they’re not the only market participants in this space). Instead of pressure pads, they offer a camera placed in a resident’s room, typically at bedside (since this is where most falls occur) and the camera then continuously monitors the resident, constantly capturing video of the resident.

However, there is never more than 10 minutes of video saved in the systems’ buffer at any one time, and video is only ever permanently saved if a fall is detected. The AI and machine learning built into the system detects falls at apparently a 94% level of accuracy, and immediately alert staff when a fall occurs. Staff are immediately able to review the video and institute fall prevention recommendations based on exactly what they see the resident do (as opposed to what they imagine happened).

Virtual Companions. This is a subject near and dear to my heart (see here, here, and here). Let’s go back to the example of Amazon’s Alexa (and it’s various competitors – like the Google Home or the Apple Homepod) – these digital assistants are useful, but they aren’t exactly companionable – more just disembodied and mildly robotic voices that do what you tell them (although Alexa can tell jokes, or sing songs for you if you ask it).
One of the other “rote” tasks in nursing (hate to put it that way) revolves around the insubstantial yet extremely important task of providing companionship to residents. The hug, the touch on the shoulder, the listening intently to the older adult as they tell a story – these are all vital to the health of OA but due to the abovementioned issue of staffing and sky-high demand for long-term care services, nursing staff are much less able to provide this service to their clients.

So, how about this?

paro with old lady.png

Figure 3. Paro robot doing its thing. Courtesy of the Toronto Star.
Above is the Paro robot – a robotic companion that uses its built-in machine learning algorithms to learn the name users give it, and to respond preferentially to being stroked, and to avoid being hit or dropped. Moreover, it’s adorable – and research tends to suggest that it delivers beneficial affects to the users (which includes calming dementia patients), by stimulating oxytocin production. Oxytocin, of course is the feel-good chemical that parents get when bonding to their children or when new mothers first nurse their babies.

Other Applications for AI and Machine Learning in Facility and Home Care?

This kind of technology has applications that literally are only limited by imagination and a few smart programmers. A worthy mention is the company Winterlight Labs, which has a proprietary assessment tool which claims to assess for the presence of dementia via speech-sample assessment of patients to a degree heretofore impossible using standard, human-administered cognitive tools. This kind of innovation has the potential to put geriatric neuropsychologists out of business!! (Well – maybe not quite yet).

Also an honorable mention goes to CareAngel – they have a system whereby the digital assistant (like Alexa) calls OA and simply asks them how they are doing (they call these “care touches,”) and then has an actual conversation with them. For example, if the older adult says “terrible, I’m in a lot of pain,” then the system asks them additional questions (like what level their pain is, where it’s located, etc). and then depending on their answers, summons a live care provider.

Bottom Line

The bottom line is that AI and machine learning are poised to revolutionize the care of OA both within and outside the LTC industry. This revolution will result in a lowering of costs, mostly in the form of less staff required for routine, rote monitoring and rounding of residents, but also – in the form of less costly trips to the emergency room or ICU due to real or even misclassified falls, as well as infections and injuries. It may even result in less need for humans to provide companionship to residents, as we might be able to offload some of that work to social robots and digital companions (as creepy, and potentially ethically questionable as that may be to some).

Nursing homes and OA care are going to see skyrocketing demand over the coming years. In order for our nation to not get completely swamped by the sheer weight of the cost and labor of caring for our most needy and vulnerable citizens, we’ll need to find ways to innovate our way out of this. The AI and machine learning revolution may in fact help us to do just that!

 

A Man Called Ove

Movie Review Time!

It’s been awhile. So, I think it’s time to go full circle and return with another movie review. If you, dear reader, recall – that was in fact how this blog was started in the first place, with my review of “Robot and Frank” with Frank Langella. The movie in that case was reviewed because I considered it both a whimsical and touching exploration of the stresses of dementia caregiving. Today I’ll be talking about “A Man Called Ove,” a Swedish-language film, from a 2012 book of the same name by Swedish author Fredrik Backman. In this case, I think it’s an incisive exploration of a somewhat different topic that’s relevant to aging – that of loss and of older adult suicide.

ove_final_poster

(Warning – spoilers ahead!)

The movie opens with the 59-year-old Ove (played by the Swedish actor Rolf Lassgård), arguing and being unpleasant and angry with a cashier at a grocery store, over a coupon to pay for flower arrangements for his late wife’s gravesite, who died six months previous to the movie’s opening. The movie, which is billed as a comedy-drama, gets a lot of laughs through Ove’s over-the-top negativistic behavior, where he variously threatens to kick and turn barking dogs into “purses” (if I remember correctly), calls at least two dozen different characters in the movies “idiots!” and at one point gets into a physical altercation with a clown at a children’s hospital.

Ove’s woes and losses are many over his 59 years – he is depicted in the opening scenes of the movie as losing his job, and also has apparently recently lost his best friend to dementia (or, at least, a severely disabling neurodegenerative illness that has rendered him largely unable to communicate). The movie depicts Ove as responding to this by becoming chronically suicidal, and throughout the movie he attempts to kill himself via multiple methods, such as hanging, carbon monoxide poisoning, and via a shotgun – and ultimately failing due to interruptions from nosy neighbors as well as the sheer difficulty of the task itself – apparently suicide is not so easy. (He comments at one point at his dead wife’s gravesite that killing oneself is actually rather difficult)!

A Man Called Ove – A Prototype of the High Suicide Risk Individual?

Some brief facts for you, at least as they regard the United States:

  • A completed suicide happens ever y thirteen minutes.
  • Our suicide rate is the highest it’s been in approximately 28 years.
  • Older adults are disproportionately represented in older adult suicides, with white males being disproportionately represented.

“A Man Called Ove,” in my view, almost-perfectly movie illustrates the prototypical high-risk individual for suicide.

In the movie, Ove becomes suicidal ostensibly so he can join his wife, Sonja, as he has supposedly promised her. However, it becomes clear that what may be driving Ove is he has been suffering the ‘death by a thousand cuts,’ multiple losses over time – his wife, his job, his child, his friend, as well as his treasured position as the leader of his small homeowners association.

This theme – that of multiple losses in an older white male leading to increased suicide risk – it is a distressingly familiar one. Aside from adding in alcohol abuse and medical / functional losses (e.g., such as loss of eyesight, or ability to walk), Ove hits just about every risk factor for older adult suicide I am aware of, at least as far as North Americans are concerned (I suspect that there may be some similarities with our Swedish counterparts).

Redemption

Hiding (sometimes very well!) underneath the curmudgeonly, depressive exterior of Ove there is a man with an almost-boundless capacity for love and attachment, as well as a man of many talents – a man with the ability to repair just about anything (cars, radiators, dishwashers), perform acts of heroism (saves the life of a man from an oncoming train, ironically as he is trying to commit suicide himself), and shows surprising acts of tenderness and caring, such as sheltering a young, newly-outed gay man who has recently been thrown out of his home.

What saves Ove, in the end, is finding connection with others. Some of the nosy neighbors include an Iranian-born pregnant wife and her bumbling husband, whom for some reason fail to be repelled and are instead charmed by Ove’s nonstop cantankerousness. Over time, he becomes part of their family and he finds within himself a reason to live. He finds connection, and he finds life.

Overall, while superficially this may look like another movie milking the “grumpy old man” shtick seen in popular culture that has in my mind bordered at times on outright ageism, I was charmed by this movie; in how it carefully and bitter-sweetly took it’s time with the character development of the protagonist, who over a series of flashbacks is depicted as coming of age and marrying the love of his life while at the same time enduring terrible, traumatic loss and yet soldiers on.

The movie ends fittingly and in a way that made my heart ache and yet practically sing at the same time – Ove died because of (no irony here!) an enlarged heart, and with a funeral service packed with well-wishers, friends, and loved ones. Ove dies – because his heart is too big.

 

Models of Psychological Care in Nursing Homes and the Effects of Medicare Reimbursement Policies

So, as a long term care psychologist, I like to tell students and staff that I am really generally quite useless on my own in such a large, chaotic care environment like a long-term-care facility. Ironic, of course, because that’s the traditional model of provision in community nursing homes, right? The psychologist scurries in, sees patients in his caseload, he / she barely has time to talk to nursing staff, the psychologist scrawls some notes in the chart, and scurries out. This basically the traditional “collaborative care” consultancy model that I operated under when I worked my first year doing nursing home consulting post-licensure.

This traditional service delivery model (again, which is basically the rule in community skilled nursing facilities) is a direct result of the way we fund the services of psychologists in nursing homes – e.g., fee-for-service via Medicare, and overwhelmingly reimbursing for direct care services only (e.g., where the psychologist is interacting directly with the patient).

Moreover, this type of service delivery model – via economics – completely disincentivizes and discourages long-term-care psychologists from doing the most valuable kind of work they have in their arsenal, which is making close, collaborative partnerships with interprofessional care staff. Psychologists do all sorts of work with staff, formal and informal, which Medicare doesn’t (as far as I’m aware) reimburse for. In terms of informal, indirect services – we do “curbside consulting,” which is what I call those informal, friendly chats at nursing stations or in breakrooms where nurses, usually in the midst of chatting about unrelated issues (what they did for the weekend, etc.) they say “oh, by the way Doctor, can I talk to you about….” (insert behavior issue here).

In terms of the more formal work psychologists do? We do in-service trainings for nursing staff (I’ve taught classes for nursing CE credits, in fact). We are resources for mental health education for staff. One service which I find very enjoyable to deliver is the STAR-VA behavior management intervention, which deliberately makes sure to include Social Work, Recreation Therapy, physician staff, and nursing – because, bottom line, psychologists are generally fairly ineffective on their own. I attend care planning meetings where I assist and consult with nursing staff in developing care plans… and then there’s all the other stuff too! (Attending nursing reports, attending administrative meetings, etc etc etc).

So, what’s the moral of this post? At least as far as I can tell (because I don’t think Medicare’s approach to reimbursing psychologists for their work has changed much over the last few years since I worked as a private consultant), Medicare doesn’t pay psychologists do the most effective work they can do. Under Medicare, geropsychologists don’t tend to do their best work (unless they’re unusually creative and charitable, perhaps?).

I’m lucky though – I get a salary for what I do here at the VA (and I get paid reasonably well). So, I get paid just as much for seeing a patient for 1:1 therapy (which I do here and there) as I do for working with staff to brainstorm a difficult behavior management issue. I’ve seen how ineffective it is to operate as a Medicare-reimbursed psychologist in community nursing homes (it’s like sticking your fingers in the proverbial dyke, really), and I’ve also experienced first hand how much more effective I can be in my job as a collocated, salaried provider. Obviously you know what my preference is.

Behavior Management for Dementia: The STAR Program

Behavior Management in Dementia and Linda Teri’s STAR program

So I think at this time I’m going to get briefly away from my current fixation on gerontechnology and gadgets and instead focus on something that’s been happening at my day job at the VA. Over at my job site we’ve instituted something called the “STAR-VA” program, which is based pretty closely on Linda Teri’s STAR and STAR-C programs (btw, STAR stands for Staff Training in Assisted living Residences).

Dr. Linda Teri’s STAR program has been trialed extensively at assisted living facilities (ALFs, as they say in the biz), nursing homes and also with family caregivers (the STAR-C program) and has been found to yield exceptionally good results. As I indicated above, it has now been ‘ported’ to the VA and although the data is somewhat preliminary, it appears that STAR-VA is an extremely effective non-pharmacological approach.

What is the STAR program? Well, you can find out by purchasing a kit over at http://depts.washington.edu/adrcweb/STAR.shtml. Or, you can get hired by the VA as a nurse or psychologist, work for a VA Community Living Center (CLC, which is VA-speak for what we all know of as skilled nursing facilities, or nursing homes), and get trained in STAR-VA by their ongoing VA Central Office (VACO) training program. Or – you can listen to my very brief overview and commentary here (although that doesn’t substitute for the above – but will give you a sense of things). I have received the STAR-VA intensive training myself, as have my colleagues at the VA facility where I am employed.

Very briefly, the STAR program is designed to address what is an endemic problem in nursing homes and ALFs, specifically what is called “behavioral and psychological symptoms of dementia” (or BPSD for short) as the technical term currently in vogue, but also often called things like “agitation” or “behavior problems.”

A representative kind of behavior that the STAR program is designed to address is a problem that’s consistent and chronic (as opposed to a one-off behavior), takes place at reasonably consistent times, and isn’t the result of an unstable underlying medical condition (e.g., such as what you might see in a delirium). A prototypical behavior is “resistiveness to care,” such as when nursing staff (typically nursing assistants) are cleaning a patient, e.g., “ADL care.”

Imagine that you’re a patient in a hospital and you have trouble walking, controlling your bowels and bladder, and require assistance to keep yourself clean and toileted. Now imagine that you require assistance from other adults (nurses) to clean you every time you have a bowel movement or need to pee. Imagine how difficult (e.g., embarrassing, anxiety-provoking, and even somewhat demoralizing) that might be!

Now imagine what would happen if you were in the same situation, and you had *dementia.* Imagine you didn’t know where you were, and how you got there. Imagine that you’re lying in a bed, confused, and all of a sudden strangers come into the room and start squawking at you and saying unintelligible things, and start pulling off your pants and grabbing at your private area.

What would *you* do?

In many cases, these patients become aggressive, will yell, scream, physically resist, and at times try to hit nursing staff. Staff will respond by trying to reason or sweet-talk the patient. They may give “time outs” (leaving the room and coming back later, hopefully when the patient has calmed down). They may medicate the patient with pain pills and/or psychiatric medications. Also, in this situation I’ve seen nursing make use of multiple staff members (ostensibly to protect the staff giving the care from physical injury). Very frequently, these interventions either don’t work or make the problem behavior worse.

How do psychologists help with these problem behaviors in dementia?

In community nursing homes, where psychologists are typically reimbursed by Medicare, and the traditional “roving consultant” model applies, psychologists are restricted to being reimbursed for largely what is direct care services only. That is, they are reimbursed for seeing patients on a 1:1 basis for psychotherapy or to provide psychological assessment. Although there is a category of “behavior management” services that psychologists can be reimbursed for, it still requires the psychologist to be seeing the patient and only reimburses for 2-3 sessions at most.

This is not a very effective model for delivering effective behavior management approaches in residential care facilities where dementia is frequently a presenting issue.

We are not hamstrung by fee-for-service restrictions on care at the VA, which is fortunate, because I know as a geropsychologist with a decade’s experience in this field that the best way to address behavior problems in dementia requires approaches that embody the following:

  • The approach must be staff-driven. At best you’ll likely only have one psychologist available for an entire nursing home or ALF, who is only there part of the day or week. Therefore, all staff must be prepared and empowered to offer effective behavior management techniques on a 24/7 basis.
  • The approach must be interdisciplinary. Behavior problems are not just a “psychology problem” or a “nursing problem.” Older adult residents at ALFs or nursing homes interact with a wide variety of staff from janitors to food service to physicians and et cetera. Behavior management is a team effort!
  • The approach requires an intense focus on staff education and even a “cultural shift” in how dementia patients are looked at and cared for. Dementia is a disease unlike any other because it affects how patients see and understand the world and interact with others, it affects everything. It has no cure. There is no effective treatment that can restore people to normal functioning. It requires lifetime management for the sufferer. In order to provide the most compassionate and effective care, staff need to understand, appreciate, and as best as possible, empathize with the plight of these patients.

So, given the above, what is does the STAR approach entail? The STAR program has within it four distinct elements which embody key principles and approaches:

  • The ABCs of Problem Behaviors. This of course refers to the old “Antecedents, Behaviors, Consequences” model of looking at behavior problems in dementia (this is also a model that behavior analysts use to address behavior issues with developmentally disabled individuals, as an aside). An example of a simple “ABC card” is here. In working through this ABC model, where staff carefully define the behavior problem, look at the Who, What, When and Where of a behavior, and look for its triggers and consequences, staff can then find ways to alter the contingencies that may be maintaining the behavior issue, and ultimately, change it. In encourages staff to look at behavior problems in dementia as a “detective work” problem (since dementia patients typically can’t tell you what the problem is).
  • Increasing pleasant events. As I’ve worked on the STAR-VA program, I have found that this is a critically important piece – namely, that behavior problems in dementia patients becomes much less likely when they are regularly exposed to personally meaningful pleasant activities. In an unrelated aside, interestingly, a tried-and-true method for treating depression in intact adults was pioneered by psychologist Peter Lewinsohn, so-called “pleasant events therapy.” In the same way that systematically increasing exposure to pleasant events can result in remission of depressive symptoms, it can also result in decreases in behavior problems in dementia – almost as if these behavior problems are how dementia patients tend to express their distress!!
  • Promoting effective communication skills. Dementia patients have problems with language. They may have trouble expressing themselves verbally, or understanding language, or both. That means for staff to stand the best chances of having themselves and their intentions understood, they need to communicate with their patients differently. They need to SLOW DOWN when they speak. Speak clearly and in short, simple sentences. They need to pay attention to their nonverbals – because with dementia patients, it matters as much (and frequently more) in terms of how you say things as much as what you’re saying.
  • Realistic expectations. Of the four STAR principles, this one is probably the most purely educational in nature, and in my opinion, one of the most critically important. For caregivers to be in the position to interact calmly and compassionately (and therefore, the most effectively) with residents who have dementia, they will need to have the most accurate understanding possible of what exactly is the matter with their patients. What is a sign that a staff member does NOT have a realistic or accurate picture of a dementia patient? When a caregiver says, “oh, they have selective memory.” Or “they are doing it on purpose.” Or “they know what they are doing.” Obviously, these are not helpful ways to look at our dementia patients. Realistically, when dementia patients act out, forget things, and perhaps are even aggressive or combative, we know it’s because their brains are sick and not working properly. If we understand things thusly, then we are much more likely to react calmly and compassionately to them then to take it personally.

In future posts, I’d like to talk more about the STAR program components in detail.

Smart Homes for Seniors

So in doing some reading about sensor technology and smart homes I encountered yet again some issues about ethics and technology acceptance both in older adults and in general.

Backing up a second – the future, of course, is already here when it comes to technology. We carry around these things called “smartphones” which contain within them technology that easily eclipses the wildest imaginations of people just a few decades ago (puts Gene Roddenberry’s communicator device thingies to shame). We’re all instantaneously connected to each other now with social media, Twitter, Facebook, etc. Disruptive technologies like AirBNB and Uber are fast-becoming a feature of the landscape.

With all of these technologies we’ve traded a portion of our privacy in exchange for what is in many cases a whopping dose of utility. This is the whole point of so many of these disruptive technologies. Imagine 20 years ago the idea of more or less offering up your apartment for short-term rental to a bunch of strangers using AirBNB, or sharing your personal life via daily updates over social media, or advertising even your garage sale over Craigslist for the world to see?

So in just the last few year alone, privacy has become really a commodity that we’re willing to trade in order to receive concrete benefits in return. (I’ll leave to the side the question of government officials essentially forcibly taking your privacy away via electronic snooping – which I consider a different animal entirely).

This takes us to the question of a potentially very useful technology for a population that, as always, is near and dear to my heart – older adults. The technology in question, broadly speaking, is sensor technology, and typically networked sensor technology (because what good are sensors if you can’t collect and manipulate the data in real-time?). Think positional sensors, such as geotagging, bed alarms, chair alarms. Think movement, such as accelerometers and zone alarms. Think physiological sensor technology, such as heart rate and respiration.

Now think about the real challenge facing us when it comes to the coming” demographic tsunami” that I’ve spoke of repeatedly in past blog posts. What seems to be increasingly clear about this wave of Baby Boomers that are currently beginning to retire and getting old is that they will be faced with a historic deficit of residential care options, particularly long term care, but also assisted living – but on the other hand, may not need these options nearly as much as previous generations due to advances in this particular cohorts ability to maintain their physical condition. This means they’ll be older, physically healthier (and therefore may not need nursing homes), but even if they need them, nursing homes and assisted living facilities will be expensive and harder to find.

This is where technology comes in. Just focusing on sensor technology – lets say my mother (who is getting a bit older) develops dementia. But, she strenuously wishes to stay in her home and remain independent for as long as possible – as do I. So I make some purchases. I outfit her oven, stove, and her faucets with sensors and shutoff systems (e.g., for example, so that if she leaves the stove on and forgets about it, it will shut off automatically – or I can do it remotely myself). I somehow tag mom (with wearable RFID tags, somehow) and set up monitors in various rooms to track her positions and give me real-time updates. I enable a perimeter alarm to let me know when she leaves the home. I give myself remote access to lighting and power within the home. I enable administrator access through my iPad which I carry around with me all the time.

There are a number of products which piece together these functions and can be enabled right now with some minimal know how and configuration, although many of these systems right now are not designed or enabled with older adult care and monitoring purposes in mind. However, there are now technology startups (such as BeClose (www.beclose.com) and Lively (www.mylively.com) which offer products tailored for elder care. Additional useful tasks these integrated, tailored systems can offer is biometrics (e.g., heart rate, respiration, step counting – think FitBit for seniors), which is valuable data that can be fed back to an older adult’s physician. These systems can also offer essentially what amounts to predictive analytic modeling of behavior – e.g., for example after a system observes a senior in their home for a given amount of time, the system can then tell the user where are the most well-traveled areas of the house (and therefore the parts of the house than need the most attention in terms of maintaining safety and livability).

These developments are completely exciting. This means that any number of adults alive today can now realistically expect to be able extend the time they’ll be living independently within their own homes significantly beyond what’s currently the norm. This could spell a reduced need for expensive facility-based care (such as skilled nursing homes or assisted living facilities) and also may provide for reduced costs of facility care as well (the as-yet-unrealized “smart nursing home” of the future).

However, technology positivism should always be tempered with some realism. First of all, there is no one-size-fits-all technology fix for any problem, and that always goes double (and then some) with older adults, who are inherently a far more diverse group than their younger adult or child counterparts – any technology solution to a “problem” of aging or dementia-related issue needs to be carefully tailored and individualized to account for specific functional, cognitive, and sensory changes in older adults.

Also, with monitoring and sensor technology comes ethical issues. How monitoring systems are deployed and used, how consent (always a tricky issue with the cognitively impaired) is navigated, all of these issues come with ethical and moral pitfalls. Moreover, older adults are by their nature more suspicious of monitoring and sensor technology than their younger adult counterparts, although their acceptance of monitoring and sensor technology tends to be tempered by their discomfort with monitoring depending on 1) who is doing the monitoring (e.g., family, physician, government) and 2) the level of invasiveness or ‘granularity’ of the monitoring (e.g. video and audio, audio only, or just positional?). Also, acceptance of monitoring is greater when, for example, an older adult is assured they are not being monitored in the bathroom. Which is tricky – because bathrooms are a frequent, if not predominant location / source of falls and accidents for older adults in the home.

I remain, of course, a gerontechnology booster. If I ever find myself in private practice, I imagine that gerontechnology consulting for in-home caregivers and crafting personalized solutions will be a big part of the work I do – and I’m excited, because the upside potential is clearly huge!

Older Adults and Sexuality… and Does Your Nursing Home have a “Pornography Policy”?

Okay, first – disclaimer. This may or may not be a NSFW (Not Safe For Work) posting (I think it is, though). I will be talking, at times, somewhat explicitly about sex here. First – onto the more general topic of sexuality and older adulthood.

There are a lot of misconceptions about older adults and sex.

First:

  • Older adults aren’t interested in sex.

Of course, that’s hooey. I am reminded of a centarian (that’s someone who is 100+ years old, BTW) who I worked with and who was a patient at one of my facilities, who was wheelchair-bound, demented, had multiple medical issues, but still found the time to occasionally (cheerfully) sexually proposition his nursing staff, and, at times, would still find the time to masturbate in his room. Of course, since his memory and executive functioning was so poor, he required the assistance of nursing staff to maintain his privacy (staff would draw his blinds for him). As an aside – there is not much difference between older adult females and males in that regard, in case you’re wondering. Sexual desire does not diminish with age.

  • Older adults can’t have sex.

While it’s true that older adult males may have trouble achieving and maintaining erections the way they did when they were younger, and older adult females may have more trouble with vaginal lubrication, these issues are surmountable and frankly aren’t really that pronounced for older adults. For example, apparently 75 percent of older adult males surveyed over the age of 70 report little or no issues with erective dysfunction as they age. They’ve still got it!

  • Older adults shouldn’t have sex.

I think there are people who actually do actually think this. Obviously there’s an ageist bias at work here. The idea is that sex is something only appropriate for the youth, and that it’s somehow unseemly or inappropriate, or perhaps even unhealthy for older adults to be engaging in sexual activity (you know, might give grandpa a heart attack or something). The fact is all the things you heard about sexual activity for us young people (relatively young people, anyways) is true for older adults. It lowers blood pressure, it makes you happier, it helps you relax – heck, it’s good exercise – all true. Like anything, if you have medical issues that specifically precludes sexual activity, that’s something that shouldn’t be ignored. However, older adults can, and if they want to – should – have sex!

  • The older adult body isn’t sexy.

Says who? Younger people?

Pornography and Sex in Long Term Care

Let’s talk about pornography. Which is really what inspired this post of mine in the first place.

One thing that’s been clear to anyone who hasn’t been living under a rock for the past 20 years is that the availability, consumption, and production of pornography in the Western world, particularly the United States, has skyrocketed.

Depending on who you ask, porn is a 10 billion dollar a year industry in the United States, making it larger than major league baseball, and even possibly Hollywood. It’s “no longer a sideshow to the mainstream…it is the mainstream,” according to NY Times columnist Frank Rich.

With the much greater accessibility of pornography, and the advent of mobile technology (tablets, smartphones, and soon – wearable technology), pornography will soon be a real issue to be grappled with in long term care. Is pornography use a normal variation in human sexual behavior, is it more akin to a ‘kink,’ ‘fetish,’ or paraphilia, or is it something else? I’ll leave that to the side for now. Suffice it to say, it’s something that’s here to stay. Pornography is something many, many adults use, both males and even a significant chunk of females. Most seem to use it without becoming addicted to it (certainly a concern), or without gravitating towards illicit forms. In many ways, pornography seems analogous to drugs and alcohol. Used responsibly, it seems to be fairly harmless to most. Arguably, I suppose. Again, to be debated at another juncture, perhaps.

A recent issue that came up in my nursing home involved an older adult male in his late 60s who is being cared for due to complications secondary to stroke. This resident, we’ll call him Bob, is wheelchair-bound, essentially paralyzed on his left side, incontinent of bowel and bladder, and requires extensive assistance with his activities of daily living (ADLs).

Bob also has been known to exhibit some behavior issues in the past, approximately a year ago he distinguished himself by being combative with nursing staff (primarily during ADL care, e.g., such as when he has been cleaned up). Although I worked with him a bit more extensively in the beginning of his time with us at the nursing home, for approximately the last year I haven’t had much contact with him, and by all indications Bob seemed to have adjusted to being at our nursing home.

However, recently I began to hear some interesting information about Bob. First, I began to hear about how Bob had begun shredding his incontinence briefs (which are basically large adult diapers) – he was doing this, of course, to access his private parts for the purposes of masturbation. Well, no big deal – we just replace the briefs after he’s done, correct? Otherwise, nursing staff were encouraged to continue to maintain Bob’s privacy when he needed it (e.g., to draw his blinds for him, that kind of thing).

Then, I heard some accounts of Bob engaging in some sexually explicit language with nursing staff, and, most recently, with one of our recreation therapy staff, e.g., asking staff members to sleep with him, things of that nature. Some of this was documented.

Shortly after this, I learned that Bob had made a specific request of his physician to obtain a portable DVD player so that he could watch “his (pornographic) movies.” This request was communicated to our recreation therapy staff (who handle offering loaner DVD players to residents) and this caused a minor controversy / uproar amongst them.

I should say that the prospect of nursing home residents accessing and using pornography is not in fact new at our VA nursing home. In fact, in doing some research on this issue (which was sparked by the case of Bob), I found out about a few of our past and current residents at the nursing home who used porn (overwhelmingly via use of portable DVD players), and was amused to find that there was an informal trade in these materials that goes on between a small subset of our patients.

That being said, in all of the cases of porn use in other residents that I found out about, all of them involved veterans who were, for the most part, *independent* in their use of pornography. They were able to place DVD discs in players, cue them up, hit the play, pause, rewind, and stop buttons, and adjust the volume (and use headphones where necessary). Aside from cues, reminders, and perhaps some assistance in drawing their blinds for privacy, these were veterans who did not require “setup assistance” in using pornography.

So, here was the difficulty with Bob. He apparently expressed an interest in using porn. Let’s say his family even supplied him some pornography. RT has an existing policy of loaning out DVD players to residents who wants them. Who’s to say that this veteran can’t access porn if he wants to?

Well, the answer was, he would need assistance with it – in other words, with this resident we would be faced with the prospect of nursing staff setting up his porn, pressing play, and perhaps even pause and rewind on his porn videos; adjusting the volume, et cetera. This is not exactly what I consider to be the job of professional nursing staff, but perhaps my view is old-fashioned and unfairly restrictive.

Of course, other issues came into play with Bob as well. He had some significant dementia, which seemed pretty clearly to result in some disinhibition leading to inappropriate behavior with our younger, female staff members – not cool. And so the question was raised (prominently by me), would facilitating Bob’s use of sexually explicit materials somehow inadvertently be fueling his sexually inappropriate behavior? Would we want to risk more of that by effectively supercharging his access to porn (e.g., by upgrading his capabilities from magazines to full-length porn videos)?

What do you think – is actively facilitating the use of porn by nursing home residents a job for nursing or ALF staff?

Before you say a horrified NO!, consider the movement towards patient-centered, individualized care and the provision of a “homelike environment” for long-term care residents. Why isn’t the use of legal pornography a recreational preference to be respected just like any other recreational preference? What makes it special unlike any other recreational activity? In my research for this blog post, I was forwarded a 1992 article from “Advance for Occupational Therapists” (sorry, haven’t figured out how to link to it – but I can email the article to you on request) where apparently an assisted living residence actually had taken it upon themselves to sanction and help run a ‘porno night’ at their facility. Which I found a bit boggling, particularly for the early 90s!!! So, attitudes on this issue can certainly range.

I could take this further – wouldn’t we consider it a form of discrimination to allow patients who are independent enough with their functioning to use porn in a nursing home, but those who are impaired in some important way, say, a quadriplegic resident – they are *not* allowed to access their pornographic materials, because they require substantial assistance from nursing staff?

So this is the quandary we were faced with. Here’s what I did – as I indicated earlier (like any dutiful VA Community Living Center geropsychologist) I queried my VA nursing home psychologist listserv, and got a number of responses.

One of the most notable included this– it’s one of the few nursing homes out there that has developed a specific protocol for addressing “sexual expression” amongst their residents, and to their credit, it spells out much more than I’ve seen from nursing homes out there on average. However, even then it’s somewhat vague as to how to treat the use of sexually explicit materials by nursing home residents:

“Residents have the right to access and/or obtain, for private use, materials with legal but sexually explicit content: books, magazines, film, video, audio, pictures, or drawings.”

So what does “access” mean, in practice? What does “private use” mean? Et cetera?

As internet use becomes more and more ubiquitous amongst older adults, and sexual attitudes continue to liberalize – I think that nursing homes will need to all have their own “sexual expressions” policies – and moreover, it will need to specifically address pornography use. I can’t see how we’ll be able to avoid it!

Online Dating and Nursing Home “Roommate Matching”

I will eventually talk about nursing homes and older adults today, but first I wanted to talk about online dating. Wait, what? Yes, I’m recently single, but that’s not why I’m on this subject.

Bear with me. So I was watching a TED Talk video recently, plus browsing some articles about predictive analytics and predictive matching algorithms. The video was here and yes, it relates to romantic matchmaking – increasingly an important way that single adults find romantic partners in this day and age.

There are a number of dating sites out there. Many take the approach of simply having their clientele create a profile, add pictures, give them access to other profiles, and then allow people to somehow message each other for dates. This is basically the approach of sites like Blendr / Badoo, or AdultFriendFinder – you see something you like, you message them. There are other sites that cater to “special interests” – such as married people who want to cheat on their spouses (AshleyMadison.com), or for specifically finding and dating school alumni (DateMySchool.com).

I wanted to focus on the sites that do predictive analytics, or predictive matching. These are sites like EHarmony.com, which uses a personality profile matching system to find you good dating partners – Eharmony was apparently founded by a psychologist and they tend to be staffed by people schooled in statistical techniques and personality theory.

Then there’s OKCupid (my personal favorite!). Unlike EHarmony, they don’t focus exclusively on personality as part of their matching algorithm. What they do is when users log on, after they create a profile they must answer a large number (at least 75, if I remember correctly) in order to optimize the algorithm and start getting good match predictions from their proprietary system. A lot of the questions seem rather random, and relate to issues like politics, lifestyle, aesthetics, etc.

So how do they construct this algorithm? Basically they do three things. First, they ask you the questions, and then you, as the user, are required to provide your answer. Second, they ask you to rate how you’d like the other person to answer (‘answers you’d accept’) and then third, they ask you to rate the importance of the question. After tabulating your answer, this allows OKC to weight your answer and then use it to calculate a match percentage. On OKC, if you have a match of 85 percent or more with someone else, its widely considered that you should probably check them out and maybe even go see ‘em for a date (of course, there’s debate about whether this faith in these proprietary matching algorithms is misplaced or not).

In practice, the OKC algorithm operates both using theory and empiricism. To a certain degree, the OKC algorithm doesn’t appear to care what the user says when they answer – it just needs to know how each person answers a particular question and then provides them with a match percentage that’s based ideally on the desired outcome (which in OKCs case, is whether you’d disabled your profile and indicated you’ve done so because you’ve successfully found someone to be exclusive with). In other words, OKC’s software is interested in the following: based on past experience of successful matches, how likely is it that a pair of potential partners who each answer a given question in a particular way or pattern going to be a successful match?

To be fair, it appears based on the little I know about the OKC algorithm, there is some theory in how they construct their questions. But I wanted to focus on empirical test design and pure empirical matching algorithms. I often think that psychologists and social scientists spend too much time on theory and not enough time on utilizing the raw predictive power potentially found in computational mathematics.

How can this apply to nursing homes? One thing that I was thinking about is the periodic issue of poor roommate matches. Here at the VA nursing home where I work, a majority of the patients are in double rooms, some are in four-person rooms. There’s a lot of issues that go into putting roommates together in nursing homes. The ‘first cut’ issues, of course, are things like microbe compatibility (MRSA positive / negative), whether a resident needs access to wall oxygen, a bariatric room (e.g., larger toilet, larger bed, etc).

Once those issues are taken care of, then the “art” of roommate matching takes over. What this looks like, in practice, is an animated discussion which takes place primarily between nurse managers and the physicians (with me occasionally joining in). It’s clearly a fun discussion for most, because of the inexact nature of it. It goes like this:

“So we should put Mr. X in with Mr. Y. Mr. X. is quiet and so is Mr. Y.”

“But Mr. Y. is African-American. Didn’t you say Mr. X has made some racial comments in the past?”

“Yes, but I’ve seen them chatting pleasantly. They both like football too.”

“I think Mr. Z. would be a better choice.”

“But Mr. Z is a night owl, and Mr. Y likes to get to sleep by 8pm.”

Et cetera.

Over the years, I’ve observed that when roommate matches go poorly, it can result in all sorts of untoward events. It can result in time and labor-intensive moves of patients and their belongings. It can result in “behavior problems.” It can result in fights. All of these things are negatives for patient health and well-being, and are an unnecessary drain on nursing resources.

Years ago, I witnessed how bad this problem can get. We had a resident (let’s call him Mr. Bob). He was a latino male, and was very sensitive about his racial background and very sensitive about sleights and perceived them frequently as being borne of racism (which may be based on many painful, real experiences he has had in his life). He also had issues with paranoia that were likely at least somewhat secondary to his previous cerebrovascular hemmorage, which had left him wheelchair-bound and with some cognitive impairments.

Well, we tried to get this gentleman properly matched up with roommates. His first roommate was a 90-plus year old gentleman and Air Force veteran, who was born and raised in the Bay Area but whom Mr. Bob immediately had trouble getting along with. They began sniping at each other almost immediately. We moved Mr. Bob, and moved him in with another older, white gentleman (most of our residents are Caucasians) and Marine veteran, but one whom we thought would be a good match with Mr. Bob has they both had asked to room with each other…. Guess what, it was even worse. The two eventually stopped speaking to each other, were calling the nursing station and hitting their call buttons constantly to complain about each other – it was a nightmare!

After a couple more moves, we found a roommate for Mr. Bob that seemed to work and he’s been fine now, more or less, for the last few years – but obviously it required a significant degree of trial-and-error to get the job done, a lot of nursing hours and time wasted, and along the way, lots of unneeded “behavior problems,” fights, and lost sleep of residents that may have been avoidable.

There is a good deal of theory and anecdote out there as to what makes for good roommate matches in the long term care environment. There’s a body of research on roommate matching that can be drawn from studies of undergraduates (who are often the preferred guineau pigs of psychology departments, since they are the most available), but that may have limited applicability to the geriatric, long-term-care crowd.

But the thing I’ve been struggling with is how to go about tracking this as an outcome. In the case of OKCupid, they have a great way of doing this – when people disable their accounts, they are asked as they are leaving, “why are you leaving?” Users are then able to tell them that they found someone, and then OKC asks them who. Bingo, there’s the outcome data, which can later be mined for variables to further optimize their matching algorithm. How would this get done in the nursing home environment?

Well, we could ask, I suppose – via questionnaires and the like (which of course now starts to sound like it would require formal research – given the issues with privacy and risk posed by “rating” each other). Of course, there’s the issue that sometimes, roommates (like romantic partners) may be attracted to each other as potential matches, but may in fact be terrible for each other (like the above example).

Sure, there are any number of potential issues, such as personality, politics, race, culture, medical issues, family visits, et cetera, that may make or break roommate matches. But until we start tracking this important outcome, I don’t think there will be a way to get a handle on this in the future.

Take Nursing Home, add a Dash of Innovative Technology and Futurism

What I want to do today is try and stitch together several of the innovations I’ve been thinking about, and try to knit them all together, so we can all sit back and imagine the idealized, technologically-savvy nursing home of the future. Futurism is fun. Given as heavily regulated as nursing homes are, in my humble opinion its difficult for a culture of innovation to thrive in these sorts of places. However, I’m going to try.

What will the nursing home of the future look like? What will it offer its residents?

Social robotics and the use of ‘virtual companions.’ Whether it’s by introducing use of devices like the Paro robot (which I’ve written about previously here), or via the use of virtual companions like ALICEBOT (friendly chatterbots), nursing homes will increasingly make use of technologies to outsource some of the work involved to keep residents happy and socially engaged in their world. Like it or not, social technologies and social robotics are niches (albeit small right now) that’s here to stay and will become only larger as time goes on. I predict that AI will allow the introduction of virtual companions in the nursing home world as well that will be increasingly convincing and useful in that regard as well. Both will allow us to stretch our limited nursing home dollars that much further.

Virtual reality. I recently attended the 2014 American Psychological Association conference and saw no less than three different booths demonstrating commercially-available virtual reality software and hardware designed for clinical purposes. That being said, use of virtual reality technologies for clinical purposes is definitely novel and is largely being used for addressing things like simple phobias and posttraumatic stress disorder (e.g., basically virtualized exposure therapy).

My thinking is that virtual reality can be used for other purposes in long term care. Some applications include:

 

  • ‘Virtualized mobility.’ Basically in all the years I’ve been working in long term care, one of the most frequent (and mournful) things I hear from my older adult clientele is that they would give anything if they could walk ‘just one more time.’ Well, what if we could do that for them? Or how about one better, what if we could provide them with the experience of running, or flying? What if we could recreate the experience of them ambulating around their own homes, or a favorite vacation spot?
  • Pain management. One of the more compelling demonstrations I’ve seen is using virtual reality, which is a very immersive, highly transportive technology (I’ve experienced it myself) as basically the ultimate distraction tool – and reserve it for specific, highly painful interventions, such as wound care (video on this approach is here), or perhaps lymphedema therapy. Wound care happens regularly at our facility.
  • ‘Virtual Snoezelen rooms.’ A really novel idea proposed by one of my Recreation Therapy colleagues (when I was excitedly recounting my experience with touring the VR booths at APA) was the idea of a virtual Snoezelen Room (more on Snoezelen rooms here). Snoezelen Rooms are basically a very systematic and well-developed method of offering dementia patients sensory stimulation, with the aim of calming and distracting them from whatever agitation or state was driving their behavioral issues. Its been found to be effective – but one of the big downsides of Snoezelen Rooms is that they tend to require something that many long term care facilities have in very short supply – physical space. Virtual Reality technology obviously is a great workaround for this basic logistical issue.

Information technology “hubs.” This was an idea I proposed a week or so ago. The idea here is that there are several simple, tried-and-true interventions for addressing behavior issues in dementia patients (or, really, just addressing risk factors for depression and loneliness in LTC). The idea here would be to outfit all residents in a nursing home with tablet computers mounted near their beds. These tablet computers could nominally function as televisions and be hooked to cable television. However, they would also be computers hooked to the internet, so that residents can:

  • Utilize videoconferencing technology to communicate with staff and family.
  • Send and receive emails.
  • Watch ‘internet TV’ like Netflix, Youtube, Hulu, etc.

However, these tablet computers could also be outfitted with specialized technology that is hooked to the facilities’ intranet. The idea here would be to allow staff to ‘push’ content to residents computers, specifically the ones who are more impaired and require more assertive intervention by staff to manage behavior issues. Family would be encouraged to supply content to nursing staff members so that staff can provide

  • ‘Reminiscence therapy.’ A tried-and-true nursing-driven mental health counseling technique (not actually psychotherapy, but related to the psychotherapeutic technique of Life Review), it involves engaging the patient in discussion of treasured, pleasant memories. Family and friends could assist staff in encouraging pleasant reminiscence by providing the following kinds of content which could be ‘pushed’ to the residents tablet computer:
    • Family photos, photos from the residents’ childhood, and other visual cues
    • Music, such as favorite songs from when the resident was younger and ‘of age’
    • Videos of family and friends, maybe favorite movies.

 

  • ‘Simulated presence therapy.’ A powerful use of internet-connected devices in residents rooms would potentially be ones where staff could push pre-recorded audio (or even video) recordings of family, friends, and others saying encouraging or calming things to residents who have dementia. Simulated presence therapy already has a powerful literature base but it’s probably not used frequently enough in long term care due to logistical and practical issues. A seamless information technology strategy implemented in the LTC environment could easily help to facilitate the use of this technique.

How would it work? Say you have a resident with dementia who becomes agitated during ADL care (e.g., cleanings after brief changes, for example). Staff already knows that this resident becomes much more calm and cooperative if he is able to hear his daughter’s voice when care is rendered. What if staff could simply tap a button on the residents room tablet computer to play various prerecorded statements by the residents daughter while they are rendering care, ones that the daughter recorded in her own home or at work on her iPhone, and emailed in to the nursing home?

Cognitive orthotics. This is an intervention with increasing popularity with younger brain-injured patients, but has some obvious applications with older adults with mild cognitive impairment or mild dementia in long term care (which I have written about previously here). The basic idea here is that (at least in my opinion!) the overriding philosophy, or goal, in long term care facilities is for residents to be as independent as possible given their physical, functional, and cognitive limitations. So, for example, if a resident requires a walker for mobility, we encourage them to use a walker, as opposed to a wheelchair. Likewise, if the resident has mild memory problems, we encourage them to use external memory aids (such as cognitive orthotics) to assist with their cognitive functioning, rather than solely depend on nursing staff to supply them with cues and reminders… which, of course, nursing staff don’t have a lot of time to do anyways!

RFID technology (and FitBits, perhaps!). The idea here is that there is often a lot of time spent trying to locate patients in the long term care environment. Some patients even engage in “wandering” or “exit seeking.” Oftentimes they are just stubbornly independent and uncooperative people (sarcasm) who want to do their own thing and visit with friends or go outside and spontaneously sunbathe (or what have you) when they are supposed to be at the nursing station for their afternoon medication pass. Or something.

Well, what if nursing staff could easily locate residents on the premises without having to physically search for them? Moreover, if residents were outfitted with three-dimensional activity tracking devices (the FitBit being an obvious commercially-available technology) staff would have available to them a wealth of information that could be used to inform care planning. For example, residents who are sundowning could be identified quickly. Weak points in the facility could be identified to improve security when it comes to persistent wanderers. These monitors might be used to more accurately alert nursing staff when residents are unsafely ambulating (as opposed to using the annoying bed and chair pressure alarms which tend to offer so many false positives!). Sleep disorders could be diagnosed more quickly via actigraphy. The possibilities, like many of the technologies proposed here, are potentially endless!

What other innovative technologies would you like to be seen employed in the nursing home of the future?