PHI news part 2

AI Predicting and Explaining the ‘Why’ in Disease

PHI focus: Corresponds to Informatics in Disease Prevention and Epidemiology

Summary:
Identifying the cause of disease(s) outside the method of randomized controlled trials is rare. Have you ever asked yourself “…why do diseases occur?”. An emerging technology in the field of Artificial Intelligence (AI) presents an opportunity for implementation of predictive analytics for effective disease prevention by investigating the cause behind cases of disease diagnoses by leveraging the power of data to unravel the complexities of disease. AI has the potential to improve clinical decisions and improving patient outcomes but that means first understanding the causal drivers of disease(s), and this area of emerging technology is called causal AI. Causal algorithms that reveal the “why?” behind disease can be a difficult task to infer causal relationships from data, but it’s a major necessity requiring organizations to invest in building data infrastructure for it. Observational data and data from practice simulations that, for example, increase treatment for potential outcome(s), can be applied to various frameworks with AI methods. Methods such as Bayesian networks, structural equation models, and potential outcome frameworks can be used to discover mechanisms of disease, treatment optimization, and social determinants of health. Social Determinants of health use causal machine learning techniques that have the capability of streamlining investments. Building more hospitals is a common example of investments that never made a difference in factors surrounding patient impact for access to health facilities and interventions that yield successful results, especially when the real issue could be arranging safe and reliable transportation in rural populations around the world. Rate of survival from certain cancers, for example colorectal cancer, can be better understood if the underlying mechanisms of that particular cancer is also understood. Targeting the right treatment for the right patient can be facilitated by identification of causal drivers and molecular drivers, drivers that also serve as biomarkers for survival.

Assessment: Surveillance Officer of Population Health and Biostatistics at World Health Organization

To know the “why?” behind disease would open many avenues for coordinating and implementing technical activities directed towards surveillance of outbreaks and health emergencies, scale up the implementation of evidence-based interventions, , analysis and quality throughout the full cycle of the disease incident(s). Causal AI algorithms would facilitate comprehensive treatment optimizations because knowing the “why?” could provide technical oversight for the implementation, monitoring and evaluation of public health policies and programs pertaining to surveillance of emerging and re-emerging infectious diseases particularly those with epidemic and pandemic potentials. Treatment optimization is facilitated by predictive models made available as decision support to care providers. The predictive models that simulate “what-if” scenarios are used to generate data about disease outcomes and disease progression under variable action sequences and health care interventions. Such models are the vehicle for individualized treatment for patients. Decision support care can prevent recommendations made for patients that have failed to take into account certain patient factors that put them at risk for receiving less care. Getting adequate data can be challenging because causal AI can only provide trustworthy conclusions about disease if the data is representative and accurate. Specifications for accuracy are met when the models can be trained on quality data representative of the right populations and then merged with other stat sets, then compared to a good control group. Causal AI algorithms could decrease unnecessary spending for healthcare initiative and decrease patient recovery times by improving predictive treatment plans for those suffering at the hand of disease(s).

 EHR Data Display Could Hinder Children at Play

PHI Focus: Corresponds to Privacy, Confidentiality, and Security; Ethics and Information Technology

Summary:
EHR usability challenges contribute to patient safety threats and events that have threated the patient. The system that contains the electronic health record containing pediatric patient information does not have to undergo testing after being customized and implemented. Glitches and EHR mistakes are acknowledged to occur with the volume of information contains in health informational technology systems that are used by hospitals and clinicians including system feedback, visual display data entry and workflow support. It is also acknowledged there are 12 major avenues of patient safety events that occur more often for pediatric patients. The purpose of an EHR is to display an array of patient health information and therein lies the first mistake when basic patient information cannot be accessed. Comments and instructions written by physicians, medication administration information and drug history in the past and present are to be stored. Poor information display conflicts automatic EHR functions that can contribute to a clinician incorrectly scheduling medical treatment and administration, even increasing the chance to seeing the wrong patient information for the wrong patient being seen. When that information conflicted there have been mistakes where nurses have administered drugs that can put the child at risk, the epitome of poor information display mistakes that lead to a myriad of medical errors. Rigorous test are needed to asses safety-related EHR protocol and usability features utilizing consistent checkpoints of the EHR lifecycle. This can prevent future incidents and can be facilitated by ONC certification testing drafting new rules as outlined in the 21st Century Cures Act.

Assessment: Senior Director / Chief Health Information and Exchange Officer
EHR functionality should require testing for the entire HER lifecycle because if so usability testing can be extended to development, implementation and customization. Rigorous testing to assess safety-related EHR protocol is an imperative factor in patient care assessment and communication amongst clinicians. The newly drafted voluntary rules for use in pediatric care seek to reduce protentional threats to patient safety are not only needed immediately but will also serve pediatric patients the advocacy and medical care that they deserve when admitted to our hospitals here in the state of Ohio. If more rigorous testing is implemented processes that identify and prioritize the needs of clinicians using the software. Age-based care can be appropriately administered with surety, in turn lessening the chance of EHR mistakes is suboptimal usability that contributes to such errors. If pediatric EHR systems are better optimized with the new rules the potential for EHR-associated patient harm could be reduced. The potential impact would be facilitated by the voluntary certification program that the Act calls upon the ONC to not only create but make more rigorous because the program is tailored to the specific needs of pediatric care. Documentation requirements and design changes could ensure that the EHR systems meet not only the needs of the clinicians but also the be reflectively accurate of the patient and patient diagnosis. EHR vendors and health care organizations can work to together to boost transparency concerning system usability rectifying the gap by decreasing the likelihood of patient harm associated with EHRs.

Video Diary

I would like to inform viewers of a medical problem called opioid addiction. In this created media piece I role play as a public health official to identify the problem and potential solutions. I even link a bi-monthly entry to informatics tools that could enhance my ability to mitigate and deal with this epidemic. I discuss the tools needed in a role-play fashion.

130 Americans die every day from an opioid overdose. There is no other medication that within a week can condemn someone to a life of addiction. Doctors have been promoting this opioid medications on the basis of fraudulent science. There is no other medication that kills so many people as opioids. At this rate at the end of 2019 the numbers extrapolated from opioid deaths from the morgue where 60 – 70 % of death cases are from overdoses of heroin and fentanyl. Covering 1/5th of any Midwest states a morgues estimates that there will be 4,000 overdoes this year and 10,000 + for a whole state. State officials are urged to declare a health emergency.

SAMHSA developed Findtreatment.gov over the last few months, could benefit more of a broad audience by relying in part on input from at-risk individuals who would actually use the online portal.  Perhaps this could increase the simplicity of the website for those affected by addiction.

Photo : An example of GIS

GIS, part of the final chosen tools to solve the problem.

Esri’s opioid story map, called The Opioid Epidemic, is a data-rich presentation that includes graphs, interactive maps and commentary that take the user on a tour of the crisis through the lens of several data sets like drug poisoning deaths, prescription-per-provider data, and statistics cross-referenced with the voting records of public officials who either did or did not support the Comprehensive Addiction and Recovery Act (CARA) — a widely supported 2016 law representing the first major federal addiction legislation in 40 years.

Survey

Chronic Disease Research Study Survey

Obesity 

Did you know that you can help prevent chronic disease? Obesity is a chronic disease and you can help prevent obesity! Education level, income and race are just a few factors that play a role in determining those at risk or have obesity and related conditions. A chronic disease is a condition that lasts 1 year or more, require ongoing medical attention, and/or limit activities of daily living. Chronic diseases, including obesity, are caused by a short list of risk behaviors such as : tobacco use and exposure to secondhand smoke, poor nutrition, lack of physical activity, and excessive alcohol use.

Did you know that health-related smartphone apps are recommended to patients with obesity? Clinicians actually recommend smartphone aps for self-monitoring of dietary and physical activity behaviors.

As a research participant your interest and engagement is part of a creative solution to develop a public health informatics tool that can help everyone make healthy choices so that the likelihood of getting a chronic disease can be reduced and quality of life can be improved. This web based and mobile application for obesity intervention and healthy lifestyles program will send nutrition, physical activity level, sleep, and sitting time information to users and clinicians of their choice to adapt face-to-face obesity interventions for a mobile app and deliver secure and effective care remotely for all genders, adolescents, and adults in America.

Traditional methods for monitoring diet and physical activity behaviors are a thing of the past ! Your answers to this survey are confidential . Your answers will inform the creation of a web based and mobile tracking app that communicates important information to clinicians working in diabetes and weight management patient care settings to improve patient outcomes.

Demographics:
1. What was your sex at birth? Was it…
a) Male
b) Female
3. Are you Hispanic, Latino/a, or Spanish origin?
a) Yes
If yes, read: Are you…
a) Mexican, Mexican American, Chicano/a
b) Puerto Rican
c) Cuban
d) Another Hispanic, Latino/a, or Spanish origin
b) No

2. Which one or more of the following would you say is your race?
a. White
b. Black or African American
c. American Indian or Alaska Native
d. Asian
i. Asian Indian
ii. Chinese
iii. Filipino
iv. Japanese
v. Korean
vi. Vietnamese
vii. Other Asian
e. Pacific Islander
i. Native Hawaiian
ii. Guamanian or Chamorro
iii. Samoan
iv. Other Pacific Islander

3. What is your age? ________ years:
a) 12 – 17 years
b) 18 – 24 years
c) 25 – 34 years
d) 35 – 44 years
e) 45 – 54 years
f) 55 – 64 years
g) 65 – 74 years
h) 75 years or above
i) Prefer not to say

4. Education (highest degree completed):
a) Never attended school or only attended kindergarten
b) Grades 1 through 8 (Elementary)
c) Grades 9 through 11 (Some high school)
d) Grade 12 or GED (High school graduate)
e) College 1 year to 3 years (Some college or technical school)
f) College 4 years or more

5. Which of these describes your personal income last year?
a) $0
b) $1 to $9 999
c) $10 000 to $24 999
d) $25 000 to 49 999
e) $50 000 to 74 999
f) $75 000 to 99 999
g) $100 000 to 149 999
h) $150 000 and greater
i) Prefer not to answer
9. What is the ZIP Code where you currently live?
_ _ _ _ _

6. Are you currently…?
a) Employed for wages
b) Self-employed
c) Out of work for 1 year or more
d) Out of work for less than 1 year
e) A Homemaker 6 A Student
f) Retired
g) Unable to work

7. About how much do you weigh without shoes?
_ _ _ _ Weight (pounds/kilograms)

8. About how tall are you without shoes?
_ _ / _ _ Height (ft / inches/meters/centimeters

9. Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor’s office or shopping?
a) Yes
b) No
c) Don’t know / Not sure
Health Status :
1. Would you say that in general your health is –
a) Excellent
b) Very Good
c) Good
d) Fair
e) Poor

Healthy Days :
1. Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?
_____ days (Enter a number between 01 – 30 )
2. Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?
_____ days (Enter a number between 01 – 30 )
3.. During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as selfcare, work, or recreation?
_____ days (Enter a number between 01 – 30 )

Health Care Access
1. Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health Service?
a) Yes
b) No
c) Don’t know / Not sure

2. Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?
a) Yes
b) No
c) Don’t know / Not sure

3. How your household usually travels to the store for your grocery shopping:
a) in my car
b) in a car that belongs to someone I live with
c) in a car that belongs to someone who lives elsewhere
d) walk
e) ride bicycle
f)bus, subway, or other public transit
g) taxi or other paid driver
h) taxi or other paid driver
i) someone else delivers groceries

3a.  Grocery shopping center location types nearby , select all that apply:

a) Farmers Market

b) Convenience store (gas station)

c) Drug store

d) Grocery store

3b. How often do you eat fast food ?

a) Once a week

b)2-3 times a week

c) More than 3 times a week

3c. How many green servings of vegetables do you have daily?

a)1 serving a day

b)2 servings a day

c)3 or more servings a day

4. About how long has it been since you last visited a doctor for a routine checkup?
a) Within the past year (anytime less than 12 months ago)
b) Within the past 2 years (1 year but less than 2 years ago)
c) Within the past 5 years (2 years but less than 5 years ago)
d) 5 or more years ago

Exercise:
1. During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?
a) Yes
b) No
c) Don’t know / Not sure

Inadequate Sleep:
1. On average, how many hours of sleep do you get in a 24-hour period?
a) _ _ Number of hours [01-24]
b) Don’t know / Not sure

Chronic Health Conditions :
1. Has a doctor, nurse, or other health professional ever told you that you had any of the following? Select all that apply:
a) Coronary Heart Disease
b) Stroke
c) Asthma
d) Cancer (any type)
e) Chronic obstructive pulmonary disease, C.O.P.D., emphysema or chronic bronchitis
f) Some form of arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia
g) Depressive disorder (including depression, major depression, dysthymia, or minor depression)
h) Not including kidney stones, bladder infection or incontinence, were you ever told you have kidney disease?
i) Diabetes

Tobacco Use :
1. How long has it been since you last smoked a cigarette, even one or two puffs?
a) Within the past month (less than 1 month ago)
b) Within the past 3 months (1 month but less than 3 months ago)
c) Within the past 6 months (3 months but less than 6 months ago)
d) Within the past year (6 months but less than 1 year ago) 05 Within the past 5 years (1 year but less than 5 years ago)
e) Within the past 10 years (5 years but less than 10 years ago) 07 10 years or more 08 Never smoked regularly 77 Don’t know / Not sure

2. Do you currently use chewing tobacco, snuff, or snus every day, some days, or not at all?
a) Every day
b) Some days
c) Not at all
d) Don’t know / Not sure

Alcohol
1. During the past 30 days, how many days per week or per month did you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage or liquor?
a)_ _ Days per week
b) _ _ Days in past 30 days
c) No drinks in past 30 days
d) Don’t know / Not sure

Welcome!

Welcome to my public health informatics portfolio where I trace the origins of public health informatics in a historical and seminal context based on four guided assignment prompts.

Click on the “Home” tab to the left to begin! 

 

Part 1: A timeline of informatics related events

I created a public health informatics timeline of my own investigation to identify significant milestones focused on informatics covering selected events from course material and my own internet-based multimedia resources .

Scroll the timeline within the frame below:

Made with Padlet

Part 2: Digital format approach to reduce chronic health barriers of rural residents

Health Behaviors, Health Care Access, Healthy Food Access, and Demographic Characteristics are avenues that present various challenges for individuals living in rural areas. These areas were considered when creating a survey to inform developing programs and promoting care through digital formats, such as online applications or “telehealth” approaches that reduce barriers to health care access for rural residents. Following a short discussion on the methodologies for creating the new PHI tool including any validation methods in an insightful opportunity to role play with creating a public health informatics survey that captures necessary information to build a PHI tool for addressing a rural chronic condition I present a self-made survey relating to weight control, specifically obesity.

Part 3: Video Diary

This podcast form of diary entry demonstrates my ability to think about a large scale Public Health problem to determine appropriate informatics tools and methodologies to address that problem. My video diary covering the Opioid Epidemic in a rural region of the United States over a two-month span. My entries were created from the viewpoint of a Public Health official in the United States attempting to mitigate this problem.

Part 4 : PHI in the news

News articles concerning public health informatics are great representations of application of seminal work in PHI on display in todays world. Following a summary of 4 individual news events is my fictitious assessment form a public health official within the field.