Can Software help Health care?

Apps, apps and more apps. Software is everything and everything runs on software.


Almost every industry in the U.S. has been disrupted by software. The health care field is not one of them.

Easily accessible consumer information makes everyone a little bit doctor. Emerging portable diagnostic devices will strengthen the transition. Are we up to it?

Not yet.

A large majority of people want to own their health information. Many want to store it online and have better control over it. Yet, most people don't want any extra work associated with updating and maintaining it. As public health record (PHR) expert Jim Tate said:

My 'dream PHR' continues to evolve. What I want now is a elegant interface which gives me a real time dynamic look into my record located somewhere in the stratosphere. I don’t want to have to do anything. Please don’t ask me to input anything or make more than 2 or 3 decisions. Make it simple, intuitive, powerful, and available on the internet and I will use it. Maybe."

Yes, we are inherently lazy, always trying to find shortcuts and reduce the amount of work to get a task done. Why spend time creating and maintaining our own records when a doctor can do it for us? Or even better, why not just live and enjoy life before we get sick?

Problem is, most of us at various stages throughout life suffer from subtle conditions like food sensitivities or allergies that doctors can't easily diagnose. They're relatively minor in severity, but if managed properly our lives would be a lot better off. So maybe all we need is a doctor who's just a mobile app away, always ready to answer our questions for free.

But will these legions of online doctors have enough insight into our everyday lives to know what we eat, what we breath, and what it is we're not saying to form an expert opinion?

Not likely. Even if we could wear mobile devices - always on, always connected, counting our steps, cataloging our night sweats, and equipped with miniature cameras to photograph what we eat - would the doctors be able to process all that information to form a useful diagnosis?

Aurametrix is an advanced analysis tool that correlates our symptoms, reactions and feelings based on what we enter into the system about our diet, exercise and conditions. Results from early usage of the tool show that even occasional sparse information - entered on days we feel better or worse than average - if properly evaluated can provide a snapshot of our health with sufficient insight to connect the dots to better health.  It's a form of collective intelligence that's already providing interesting discoveries without the need for us to know all the details. For example, it already knows what our foods consist of, how our daily activities or feelings align with past events, and that there are commonalities among many different things.

The future is already here but are we ready for the future?

REFERENCES

Archer N, Fevrier-Thomas U, Lokker C, McKibbon KA, & Straus SE (2011). Personal health records: a scoping review. Journal of the American Medical Informatics Association : JAMIA, 18 (4), 515-22 PMID: 21672914

Kim J, Bates DW. Analysis of the definition and utility of personal health records using q methodology. J Med Internet Res. 2011 Nov 29;13(4):e106.

Geissbuhler A, Kimura M, Kulikowski CA, Murray PJ, Ohno-Machado L, Park HA, Haux R.
Confluence of disciplines in health informatics: an international perspective. Methods Inf Med. 2011 Dec 6;50(6):545-55.

Macedo LG, Maher CH, Latimer J, McAuley JH. Feasibility of using Short Message Service (SMS) to collect pain outcomes in a low back pain clinical trial. Spine (Phila Pa 1976). 2011 Dec 3.

Lo Piparo E, Worth A, Manibusan M, Yang C, Schilter B, Mazzatorta P, Jacobs MN, Steinkellner H, Mohimont L. Use of computational tools in the field of food safety. Regul Toxicol Pharmacol. 2011 Aug;60(3):354-62. Epub 2011 May 12.

Benito PJ, Neiva C, González-Quijano PS, Cupeiro R, Morencos E, Peinado AB. Validation of the SenseWear armband in circuit resistance training with different loads. Eur J Appl Physiol. 2011 Dec 6.

Yoshiaki Sugawara,Chie Sugimoto, Sachiko Minabe, Yoshie Iura, Mai Okazaki, Natuki Nakagawa, Miwa Seto, Saki Maruyama, Miki Hirano and Ichiro Kitayama. Use of Human Senses as Sensors. Sensors. 2009, 9(5), 3184-3204; doi:10.3390/s90503184

Danger, Will Robinson!!! or injury prevention with sensors and algorithms

Health is determined by many factors including:

• Behavior (Physical Activity, Eating habits, Tobacco or substance abuse, responsible sexual choices, etc)
• Mental Health 
• Injury and Violence 
• Environmental Quality 
• Preventative measures such as immunization 
• Access to Health Care

All these factors are quantifiable, predictable and preventable. Injuries are most likely to be  perceived as “accidents” and “acts of fate”,  but they depend on the same determinants as other health factors: individual behavior, social and physical environment.  The likelihood of injuries -  unintentional ones and those caused by acts of violence - can be computed from physical location (estimations for USA¹ are a good example), gene-environment interactions², prior medical history, and physical traits³.

There are many ways to prevent injuries - just say "no" to risky behaviors, wear preventative gear while playing sports or fall-optimized shoes for elderly, watch out for others engaged in similar activities... Yet, sometimes we forget to watch, don't have access to histories of others or get diverted.  Would a body sensor or a gadget with smart software be able to warn us about potential accidents ahead to help prevent accidents?  What would it need to measure?

Software and devices automatically detecting and reporting accidents already exist:

Halo Monitoring's fall detection system, for example,  consists of a chest strap and belt clip with motionOnStar or mbrace that "intelligently integrate the driver, the vehicle and the environment" - capabilities such as this will be provided in the area of next-generation health management systems like Aurametrix.
sensors, heart rate and skin temperature monitors. Although the system detects falls only after they happen, a study showed that just the fact of wearing it increases alertness of seniors and reduces the number of falls. Although fall detection systems are not as advanced as telematics for cars - like
Unprecedented accumulation of data  - such as snapshots of driving behavior or 1.2 million person-years of hip fracture observations (Kanis JA) allows development of smarter software able to predict injuries.  Logistic regression models (Kononen et al., 2011) predict seriousness of auto accidents,  first-principles mathematical models (such as AHAAH for the ear) connect forces with injuries, neural net and other data mining approaches foretell which juvenile offenders are likely to return to crime (source of  "intentional injuries"), or allow to calculate risk of fractures based on milk intake, personal history of accidents and body mass index. Self-quantifiers such as René Ghosh are able to figure out how to use their own data to predict future injuries. Using simple math (Riegel equation bringing all running logs on to a comparable level) and trend analysis, he tied his accidents to wanes following waxes in running performance. Researchers keep refining the variables predicting injuries.  Swanenburg et al., for example, predicted multiple falls for those with a history of multiple falls (odds ratio, 5.6) and use of multiple medications (odds ratio, 2.3). And there is another simple measurement of standing position helpful in prediction. Frequent fallers, indeed, have a narrower stance width than non-fallers.


In the always-connected smart-sensor-equipped future, things such as Intel's magic carpet - picking up the weight, angle and pressure of steps - will be a commodity. Gene tests predicting injuries will be integrated with data coming from our carpets, clothing, footwear and location information. And this may be sooner than you think.


References

1. Centers for Disease Control and Prevention (CDC), National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS); 2010 Mar 4 Available from: http://www.cdc.gov/injury/wisqars/index.html

2. Husted JA, Ahmed R, Chow EW, Brzustowicz LM, Bassett AS. Childhood trauma and genetic factors in familial schizophrenia associated with the NOS1AP gene. Schizophr Res. 2010 Aug;121(1-3):187-92. PMID: 20541371

3. Swanenburg J, de Bruin ED, Uebelhart D, & Mulder T (2010). Falls prediction in elderly people: a 1-year prospective study. Gait & posture, 31 (3), 317-21 PMID: 20047833

4. Kononen DW, Flannagan CA, Wang SC. Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accid Anal Prev. 2011 Jan;43(1):112-22. PMID: 2109430

5. Price GR. Predicting mechanical damage to the organ of Corti. Hear Res. 2007 Apr;226(1-2):5-13. Epub 2006 Sep 15.PMID: 16978813

You are the Chosen One, at least by your bacteria

Host genomics is not the main decision-making factor for bacteria immigrating into human body, but  it is an important factor. Two papers recently published in the Proceedings of the National Academy of Sciences help to understand why you are chosen and how the choosers make their decisions.

Benson et al studied microbes of mice C57BL/6J, HR and their offspring. BL6 is a common inbred line prone to diet-induced obesity, type 2 diabetes, and atherosclerosis. They also develop age-related hearing loss, if are not following recommended dietary allowance. High runner (HR) mice is lean and fit and loves to exercise - it's in the genes.

Noninvasive 16S RNA sequencing (Roche 454) showed that the abundance of microbes in "core measurable microbiota" depended on 530 host SNPs, mostly those located in 13 quantitative trait loci and was influenced by 5 more QTLs.

Some of the genetic regions appear to determine what kind of bacteria immigrate and strive in the host, other regions influence the immigration rate, attracting a wide variety pf or specific nationalities. Supplementary material elaborates on  sources of variation and genotype frequencies at given SNP locations. 

How are bacteria making their decisions to colonize or not to colonize?

In another PNAS article, Ben-Jacob and Schultz explain why microbes could be smarter than humans. We may think that our decisions are well thought and sophisticated, but we are, indeed, influenced by other people and our over-interpretations of other people's reactions. Bacteria can assess the noisy and stressful environment around them more objectively and rationally. They anticipate possible drastic changes in the environment and find the best decisions by providing every bacterium with the freedom to choose its own fate. This may look like throwing dice, but the colony manages the odds and effectively programs the effect of the noise on the gene circuit performance.

Our genes may be shaping microbial communities that could, in their turn, control our physical and mental health. Yet our lifestyle choices could break the patterns and let us decide what types of bacteria we want to live with.

And for those whose fight against unwanted microbes is too hard, there may be light in the end of the tunnel: Personal Genomes project has just announced a new collaboration with Rob Knight and Noah Fierer that will enable to explore the microbial diversity of various habitats of the human body and correlate it to the genotype.

References

• Andrew K. Benson,, Scott A. Kelly,, Ryan Legge,, Fangrui Ma,, Soo Jen Low,, Jaehyoung Kim,, Min Zhang,, Phaik Lyn Oh,, Derrick Nehrenberg,, Kunjie Hu,, Stephen D. Kachman,, Etsuko N. Moriyama,, Jens Walter,, Daniel A. Peterson,, & Daniel Pomp10.1073/pnas.1007028107 (2010). Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors Proceedings of the National Academy of Sciences of the United States of America, Published online before print October 11, 2010
• Ben-Jacob E, &; Schultz D (2010). Bacteria determine fate by playing dice with controlled odds. Proceedings of the National Academy of Sciences of the United States of America, 107 (30), 13197-8 PMID: 20660309