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Posts Tagged ‘patient outcomes’


NIMHD welcomes nine new members to the National Advisory Council on Minority Health and Health Disparities

Reporter: Stephen J. Williams, Ph.D.

The National Institute on Minority Health and Health Disparities (NIMHD) has announced the appointment of nine new members to the National Advisory Council on Minority Health and Health Disparities (NACMHD), NIMHD’s principal advisory board. Members of the council are drawn from the scientific, medical, and lay communities, so they offer diverse perspectives on minority health and health disparities.

The NACMHD, which meets three times a year on the National Institutes of Health campus, Bethesda, Maryland, advises the secretary of Health and Human Services and the directors of NIH and NIMHD on matters related to NIMHD’s mission. The council also conducts the second level of review of grant applications and cooperative agreements for research and training and recommends approval for projects that show promise of making valuable contributions to human knowledge.

The next meeting of the NACMHD will be held on Thursday, Sept. 10, 8:30 a.m.-5:00 p.m. on the NIH campus. The meeting will be available on videocast at http://www.videocast.nih.gov.

NIMHD Director Eliseo J. Pérez-Stable, M.D., is pleased to welcome the following new members

Margarita Alegría, Ph.D., is the director of the Center for Multicultural Mental Health Research at Cambridge Health Alliance and a professor in the department of psychiatry at Harvard Medical School, Boston. She has devoted her career to researching disparities in mental health and substance abuse services, with the goal of improving access to and equity and quality of these services for disadvantaged and minority populations.

Maria Araneta, Ph.D., a perinatal epidemiologist, is a professor in the Department of Family and Preventive Medicine at the University of California, San Diego. Her research interests include maternal/pediatric HIV/AIDS, birth defects, and ethnic health disparities in type 2 diabetes, regional fat distribution, cardiovascular disease, and metabolic abnormalities.

Judith Bradford, Ph.D., is director of the Center for Population Research in LGBT Health and she co-chairs The Fenway Institute, Boston. Dr. Bradford has participated in health research since 1984, working with public health programs and community-based organizations to conduct studies on lesbian, gay, bisexual, and transgender people and racial minority communities and to translate the results into programs to reduce health disparities.

Linda Burhansstipanov, Dr.P.H., has worked in public health since 1971, primarily with Native American issues. She is a nationally recognized educator on cancer prevention, community-based participatory research, navigation programs, cultural competency, evaluation, and other topics. Dr. Burhansstipanov worked with the Anschutz Medical Center Cancer Research Center — now the University of Colorado Cancer Research Center — in Denver for five years before founding Native American Cancer Initiatives, Inc., and the Native American Cancer Research Corporation.

Sandro Galea, M.D., a physician and epidemiologist, is the dean and a professor at the Boston University School of Public Health. Prior to his appointment at Boston University, Dr. Galea served as the Anna Cheskis Gelman and Murray Charles Gelman Professor and chair of the Department of Epidemiology at the Columbia University Mailman School of Public Health, New York City. His research focuses on the causes of brain disorders, particularly common mood and anxiety disorders, and substance abuse.

Linda Greene, J.D., is Evjue Bascom Professor of Law at the University of Wisconsin–Madison Law School. Her teaching and academic scholarship include constitutional law, civil procedure, legislation, civil rights, and sports law. Most recently, she was the vice chancellor for equity, diversity, and inclusion at the University of California, San Diego.

Ross A. Hammond, Ph.D., a senior fellow in the Economic Studies Program at the Brookings Institution, Washington, D.C., is also director of the Center on Social Dynamics and Policy. His primary area of expertise is using mathematical and computational methods from complex systems science to model complex dynamics in economic, social, and public health systems. His current research topics include obesity etiology and prevention, tobacco control, and behavioral epidemiology.

Hilton Hudson, II, M.D., is chief of cardiothoracic surgery at Franciscan Healthcare, Munster, Indiana and a national ambassador for the American Heart Association. He also is the founder of Hilton Publishing, Inc., a national publisher dedicated to producing content on solutions related to health, wellness, and education for people in underserved communities. Dr. Hilton’s book, “The Heart of the Matter: The African American Guide to Heart Disease, Heart Treatment and Heart Wellness” has impacted at-risk patients nationwide.

Brian M. Rivers, Ph.D., M.P.H., currently serves on the research faculty at the H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. He is also an assistant professor in the Department of Oncologic Sciences at the University of South Florida College of Medicine, Tampa. Dr. Rivers’ research efforts include examination of unmet educational and psychosocial needs and the development of communication tools, couple-centered interventions, and evidence-based methods to convey complex information to at-risk populations across the cancer continuum.

NIMHD is one of NIH’s 27 Institutes and Centers. It leads scientific research to improve minority health and eliminate health disparities by conducting and supporting research; planning, reviewing, coordinating, and evaluating all minority health and health disparities research at NIH; promoting and supporting the training of a diverse research workforce; translating and disseminating research information; and fostering collaborations and partnerships. For more information about NIMHD, visit http://www.nimhd.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

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Twitter is Becoming a Powerful Tool in Science and Medicine

 Curator: Stephen J. Williams, Ph.D.

Updated 4/2016

Life-cycle of Science 2

A recent Science article (Who are the science stars of Twitter?; Sept. 19, 2014) reported the top 50 scientists followed on Twitter. However, the article tended to focus on the use of Twitter as a means to develop popularity, a sort of “Science Kardashian” as they coined it. So the writers at Science developed a “Kardashian Index (K-Index) to determine scientists following and popularity on Twitter.

Now as much buzz Kim Kardashian or a Perez Hilton get on social media, their purpose is solely for entertainment and publicity purposes, the Science sort of fell flat in that it focused mainly on the use of Twitter as a metric for either promotional or public outreach purposes. A notable scientist was mentioned in the article, using Twitter feed to gauge the receptiveness of his presentation. In addition, relying on Twitter for effective public discourse of science is problematic as:

  • Twitter feeds are rapidly updated and older feeds quickly get buried within the “Twittersphere” = LIMITED EXPOSURE TIMEFRAME
  • Short feeds may not provide the access to appropriate and understandable scientific information (The Science Communication Trap) which is explained in The Art of Communicating Science: traps, tips and tasks for the modern-day scientist. “The challenge of clearly communicating the intended scientific message to the public is not insurmountable but requires an understanding of what works and what does not work.” – from Heidi Roop, G.-Martinez-Mendez and K. Mills

However, as highlighted below, Twitter, and other social media platforms are being used in creative ways to enhance the research, medical, and bio investment collaborative, beyond a simple news-feed.  And the power of Twitter can be attributed to two simple features

  1. Ability to organize – through use of the hashtag (#) and handle (@), Twitter assists in the very important task of organizing, indexing, and ANNOTATING content and conversations. A very great article on Why the Hashtag in Probably the Most Powerful Tool on Twitter by Vanessa Doctor explains how hashtags and # search may be as popular as standard web-based browser search. Thorough annotation is crucial for any curation process, which are usually in the form of database tags or keywords. The use of # and @ allows curators to quickly find, index and relate disparate databases to link annotated information together. The discipline of scientific curation requires annotation to assist in the digital preservation, organization, indexing, and access of data and scientific & medical literature. For a description of scientific curation methodologies please see the following links:

Please read the following articles on CURATION

The Methodology of Curation for Scientific Research Findings

Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

Science and Curation: The New Practice of Web 2.0

  1. Information Analytics

Multiple analytic software packages have been made available to analyze information surrounding Twitter feeds, including Twitter feeds from #chat channels one can set up to cover a meeting, product launch etc.. Some of these tools include:

Twitter Analytics – measures metrics surrounding Tweets including retweets, impressions, engagement, follow rate, …

Twitter Analytics – Hashtags.org – determine most impactful # for your Tweets For example, meeting coverage of bioinvestment conferences or startup presentations using #startup generates automatic retweeting by Startup tweetbot @StartupTweetSF.

 

  1. Tweet Sentiment Analytics

Examples of Twitter Use

A. Scientific Meeting Coverage

In a paper entitled Twitter Use at a Family Medicine Conference: Analyzing #STFM13 authors Ranit Mishori, MD, Frendan Levy, MD, and Benjamin Donvan analyzed the public tweets from the 2013 Society of Teachers of Family Medicine (STFM) conference bearing the meeting-specific hashtag #STFM13. Thirteen percent of conference attendees (181 users) used the #STFM13 to share their thoughts on the meeting (1,818 total tweets) showing a desire for social media interaction at conferences but suggesting growth potential in this area. As we have also seen, the heaviest volume of conference-tweets originated from a small number of Twitter users however most tweets were related to session content.

However, as the authors note, although it is easy to measure common metrics such as number of tweets and retweets, determining quality of engagement from tweets would be important for gauging the value of Twitter-based social-media coverage of medical conferences.

Thea authors compared their results with similar analytics generated by the HealthCare Hashtag Project, a project and database of medically-related hashtag use, coordinated and maintained by the company Symplur.  Symplur’s database includes medical and scientific conference Twitter coverage but also Twitter usuage related to patient care. In this case the database was used to compare meeting tweets and hashtag use with the 2012 STFM conference.

These are some of the published journal articles that have employed Symplur (www.symplur.com) data in their research of Twitter usage in medical conferences.

B. Twitter Usage for Patient Care and Engagement

Although the desire of patients to use and interact with their physicians over social media is increasing, along with increasing health-related social media platforms and applications, there are certain obstacles to patient-health provider social media interaction, including lack of regulatory framework as well as database and security issues. Some of the successes and issues of social media and healthcare are discussed in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

However there is also a concern if social media truly engages the patient and improves patient education. In a study of Twitter communications by breast cancer patients Tweeting about breast cancer, authors noticed Tweeting was a singular event. The majority of tweets did not promote any specific preventive behavior. The authors concluded “Twitter is being used mostly as a one-way communication tool.” (Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month. Thackeray R1, Burton SH, Giraud-Carrier C, Rollins S, Draper CR. BMC Cancer. 2013;13:508).

In addition a new poll by Harris Interactive and HealthDay shows one third of patients want some mobile interaction with their physicians.

Some papers cited in Symplur’s HealthCare Hashtag Project database on patient use of Twitter include:

C. Twitter Use in Pharmacovigilance to Monitor Adverse Events

Pharmacovigilance is the systematic detection, reporting, collecting, and monitoring of adverse events pre- and post-market of a therapeutic intervention (drug, device, modality e.g.). In a Cutting Edge Information Study, 56% of pharma companies databases are an adverse event channel and more companies are turning to social media to track adverse events (in Pharmacovigilance Teams Turn to Technology for Adverse Event Reporting Needs). In addition there have been many reports (see Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter) that show patients are frequently tweeting about their adverse events.

There have been concerns with using Twitter and social media to monitor for adverse events. For example FDA funded a study where a team of researchers from Harvard Medical School and other academic centers examined more than 60,000 tweets, of which 4,401 were manually categorized as resembling adverse events and compared with the FDA pharmacovigilance databases. Problems associated with such social media strategy were inability to obtain extra, needed information from patients and difficulty in separating the relevant Tweets from irrelevant chatter.  The UK has launched a similar program called WEB-RADR to determine if monitoring #drug_reaction could be useful for monitoring adverse events. Many researchers have found the adverse-event related tweets “noisy” due to varied language but had noticed many people do understand some principles of causation including when adverse event subsides after discontinuing the drug.

However Dr. Clark Freifeld, Ph.D., from Boston University and founder of the startup Epidemico, feels his company has the algorithms that can separate out the true adverse events from the junk. According to their web site, their algorithm has high accuracy when compared to the FDA database. Dr. Freifeld admits that Twitter use for pharmacovigilance purposes is probably a starting point for further follow-up, as each patient needs to fill out the four-page forms required for data entry into the FDA database.

D. Use of Twitter in Big Data Analytics

Published on Aug 28, 2012

http://blogs.ischool.berkeley.edu/i29…

Course: Information 290. Analyzing Big Data with Twitter
School of Information
UC Berkeley

Lecture 1: August 23, 2012

Course description:
How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.

This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.

Other posts on this site on USE OF SOCIAL MEDIA AND TWITTER IN HEALTHCARE and Conference Coverage include:

Methodology for Conference Coverage using Social Media: 2014 MassBio Annual Meeting 4/3 – 4/4 2014, Royal Sonesta Hotel, Cambridge, MA

Strategy for Event Joint Promotion: 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

REAL TIME Cancer Conference Coverage: A Novel Methodology for Authentic Reporting on Presentations and Discussions launched via Twitter.com @ The 2nd ANNUAL Sachs Cancer Bio Partnering & Investment Forum in Drug Development, 19th March 2014 • New York Academy of Sciences • USA

PCCI’s 7th Annual Roundtable “Crowdfunding for Life Sciences: A Bridge Over Troubled Waters?” May 12 2014 Embassy Suites Hotel, Chesterbrook PA 6:00-9:30 PM

CRISPR-Cas9 Discovery and Development of Programmable Genome Engineering – Gabbay Award Lectures in Biotechnology and Medicine – Hosted by Rosenstiel Basic Medical Sciences Research Center, 10/27/14 3:30PM Brandeis University, Gerstenzang 121

Tweeting on 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

https://pharmaceuticalintelligence.com/press-coverage/

Statistical Analysis of Tweet Feeds from the 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

What VCs Think about Your Pitch? Panel Summary of 1st Pitch Life Science Philly

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

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Larry H Bernstein, MD
Leaders in Pharmaceutical Intelligence

 

I call attention to an interesting article that just came out.   The estimate of improved costsavings in healthcare and diagnostic accuracy is extimated to be substantial.   I have written about the unused potential that we have not yet seen.  In short, there is justification in substantial investment in resources to this, as has been proposed as a critical goal.  Does this mean a reduction in staffing?  I wouldn’t look at it that way.  The two huge benefits that would accrue are:

 

  1. workflow efficiency, reducing stress and facilitating decision-making.
  2. scientifically, primary knowledge-based  decision-support by well developed algotithms that have been at the heart of computational-genomics.

 

 

 

Can computers save health care? IU research shows lower costs, better outcomes

Cost per unit of outcome was $189, versus $497 for treatment as usual

 Last modified: Monday, February 11, 2013

 

BLOOMINGTON, Ind. — New research from Indiana University has found that machine learning — the same computer science discipline that helped create voice recognition systems, self-driving cars and credit card fraud detection systems — can drastically improve both the cost and quality of health care in the United States.

 

 

 Physicians using an artificial intelligence framework that predicts future outcomes would have better patient outcomes while significantly lowering health care costs.

 

 

Using an artificial intelligence framework combining Markov Decision Processes and Dynamic Decision Networks, IU School of Informatics and Computing researchers Casey Bennett and Kris Hauser show how simulation modeling that understands and predicts the outcomes of treatment could

 

  • reduce health care costs by over 50 percent while also
  • improving patient outcomes by nearly 50 percent.

 

The work by Hauser, an assistant professor of computer science, and Ph.D. student Bennett improves upon their earlier work that

 

  • showed how machine learning could determine the best treatment at a single point in time for an individual patient.

 

By using a new framework that employs sequential decision-making, the previous single-decision research

 

  • can be expanded into models that simulate numerous alternative treatment paths out into the future;
  • maintain beliefs about patient health status over time even when measurements are unavailable or uncertain; and
  • continually plan/re-plan as new information becomes available.

In other words, it can “think like a doctor.”  (Perhaps better because of the limitation in the amount of information a bright, competent physician can handle without error!)

 

“The Markov Decision Processes and Dynamic Decision Networks enable the system to deliberate about the future, considering all the different possible sequences of actions and effects in advance, even in cases where we are unsure of the effects,” Bennett said.  Moreover, the approach is non-disease-specific — it could work for any diagnosis or disorder, simply by plugging in the relevant information.  (This actually raises the question of what the information input is, and the cost of inputting.)

 

The new work addresses three vexing issues related to health care in the U.S.:

 

  1. rising costs expected to reach 30 percent of the gross domestic product by 2050;
  2. a quality of care where patients receive correct diagnosis and treatment less than half the time on a first visit;
  3. and a lag time of 13 to 17 years between research and practice in clinical care.

  Framework for Simulating Clinical Decision-Making

 

“We’re using modern computational approaches to learn from clinical data and develop complex plans through the simulation of numerous, alternative sequential decision paths,” Bennett said. “The framework here easily out-performs the current treatment-as-usual, case-rate/fee-for-service models of health care.”  (see the above)

 

Bennett is also a data architect and research fellow with Centerstone Research Institute, the research arm of Centerstone, the nation’s largest not-for-profit provider of community-based behavioral health care. The two researchers had access to clinical data, demographics and other information on over 6,700 patients who had major clinical depression diagnoses, of which about 65 to 70 percent had co-occurring chronic physical disorders like diabetes, hypertension and cardiovascular disease.  Using 500 randomly selected patients from that group for simulations, the two

 

  • compared actual doctor performance and patient outcomes against
  • sequential decision-making models

using real patient data.

They found great disparity in the cost per unit of outcome change when the artificial intelligence model’s

 

  1. cost of $189 was compared to the treatment-as-usual cost of $497.
  2. the AI approach obtained a 30 to 35 percent increase in patient outcomes
Bennett said that “tweaking certain model parameters could enhance the outcome advantage to about 50 percent more improvement at about half the cost.”

 

While most medical decisions are based on case-by-case, experience-based approaches, there is a growing body of evidence that complex treatment decisions might be effectively improved by AI modeling.  Hauser said “Modeling lets us see more possibilities out to a further point –  because they just don’t have all of that information available to them.”  (Even then, the other issue is the processing of the information presented.)

 

 

Using the growing availability of electronic health records, health information exchanges, large public biomedical databases and machine learning algorithms, the researchers believe the approach could serve as the basis for personalized treatment through integration of diverse, large-scale data passed along to clinicians at the time of decision-making for each patient. Centerstone alone, Bennett noted, has access to health information on over 1 million patients each year. “Even with the development of new AI techniques that can approximate or even surpass human decision-making performance, we believe that the most effective long-term path could be combining artificial intelligence with human clinicians,” Bennett said. “Let humans do what they do well, and let machines do what they do well. In the end, we may maximize the potential of both.”

 

 

Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach” was published recently in Artificial Intelligence in Medicine. The research was funded by the Ayers Foundation, the Joe C. Davis Foundation and Indiana University.

 

For more information or to speak with Hauser or Bennett, please contact Steve Chaplin, IU Communications, at 812-856-1896 or stjchap@iu.edu.

 

 

IBM Watson Finally Graduates Medical School

 

It’s been more than a year since IBM’s Watson computer appeared on Jeopardy and defeated several of the game show’s top champions. Since then the supercomputer has been furiously “studying” the healthcare literature in the hope that it can beat a far more hideous enemy: the 400-plus biomolecular puzzles we collectively refer to as cancer.

 

 

 

Anomaly Based Interpretation of Clinical and Laboratory Syndromic Classes

Larry H Bernstein, MD, Gil David, PhD, Ronald R Coifman, PhD.  Program in Applied Mathematics, Yale University, Triplex Medical Science.

 

 Statement of Inferential  Second Opinion

 Realtime Clinical Expert Support and Validation System

Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman, in the Yale University Applied Mathematics Program, a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides
  • empirical medical reference and suggests quantitative diagnostics options.

Background

The current design of the Electronic Medical Record (EMR) is a linear presentation of portions of the record by
  • services, by
  • diagnostic method, and by
  • date, to cite examples.

This allows perusal through a graphical user interface (GUI) that partitions the information or necessary reports in a workstation entered by keying to icons.  This requires that the medical practitioner finds

  • the history,
  • medications,
  • laboratory reports,
  • cardiac imaging and EKGs, and
  • radiology
in different workspaces.  The introduction of a DASHBOARD has allowed a presentation of
  • drug reactions,
  • allergies,
  • primary and secondary diagnoses, and
  • critical information about any patient the care giver needing access to the record.
 The advantage of this innovation is obvious.  The startup problem is what information is presented and how it is displayed, which is a source of variability and a key to its success.

Proposal

We are proposing an innovation that supercedes the main design elements of a DASHBOARD and
  • utilizes the conjoined syndromic features of the disparate data elements.
So the important determinant of the success of this endeavor is that it facilitates both
  1. the workflow and
  2. the decision-making process
  • with a reduction of medical error.
 This has become extremely important and urgent in the 10 years since the publication “To Err is Human”, and the newly published finding that reduction of error is as elusive as reduction in cost.  Whether they are counterproductive when approached in the wrong way may be subject to debate.
We initially confine our approach to laboratory data because it is collected on all patients, ambulatory and acutely ill, because the data is objective and quality controlled, and because
  • laboratory combinatorial patterns emerge with the development and course of disease.  Continuing work is in progress in extending the capabilities with model data-sets, and sufficient data.
It is true that the extraction of data from disparate sources will, in the long run, further improve this process.  For instance, the finding of both ST depression on EKG coincident with an increase of a cardiac biomarker (troponin) above a level determined by a receiver operator curve (ROC) analysis, particularly in the absence of substantially reduced renal function.
The conversion of hematology based data into useful clinical information requires the establishment of problem-solving constructs based on the measured data.  Traditionally this has been accomplished by an intuitive interpretation of the data by the individual clinician.  Through the application of geometric clustering analysis the data may interpreted in a more sophisticated fashion in order to create a more reliable and valid knowledge-based opinion.
The most commonly ordered test used for managing patients worldwide is the hemogram that often incorporates the review of a peripheral smear.  While the hemogram has undergone progressive modification of the measured features over time the subsequent expansion of the panel of tests has provided a window into the cellular changes in the production, release or suppression of the formed elements from the blood-forming organ to the circulation.  In the hemogram one can view data reflecting the characteristics of a broad spectrum of medical conditions.
Progressive modification of the measured features of the hemogram has delineated characteristics expressed as measurements of
  • size,
  • density, and
  • concentration,
resulting in more than a dozen composite variables, including the
  1. mean corpuscular volume (MCV),
  2. mean corpuscular hemoglobin concentration (MCHC),
  3. mean corpuscular hemoglobin (MCH),
  4. total white cell count (WBC),
  5. total lymphocyte count,
  6. neutrophil count (mature granulocyte count and bands),
  7. monocytes,
  8. eosinophils,
  9. basophils,
  10. platelet count, and
  11. mean platelet volume (MPV),
  12. blasts,
  13. reticulocytes and
  14. platelet clumps,
  15. perhaps the percent immature neutrophils (not bands)
  16. as well as other features of classification.
The use of such variables combined with additional clinical information including serum chemistry analysis (such as the Comprehensive Metabolic Profile (CMP)) in conjunction with the clinical history and examination complete the traditional problem-solving construct. The intuitive approach applied by the individual clinician is limited, however,
  1. by experience,
  2. memory and
  3. cognition.
The application of rules-based, automated problem solving may provide a more reliable and valid approach to the classification and interpretation of the data used to determine a knowledge-based clinical opinion.
The classification of the available hematologic data in order to formulate a predictive model may be accomplished through mathematical models that offer a more reliable and valid approach than the intuitive knowledge-based opinion of the individual clinician.  The exponential growth of knowledge since the mapping of the human genome has been enabled by parallel advances in applied mathematics that have not been a part of traditional clinical problem solving.  In a univariate universe the individual has significant control in visualizing data because unlike data may be identified by methods that rely on distributional assumptions.  As the complexity of statistical models has increased, involving the use of several predictors for different clinical classifications, the dependencies have become less clear to the individual.  The powerful statistical tools now available are not dependent on distributional assumptions, and allow classification and prediction in a way that cannot be achieved by the individual clinician intuitively. Contemporary statistical modeling has a primary goal of finding an underlying structure in studied data sets.
In the diagnosis of anemia the variables MCV,MCHC and MCH classify the disease process  into microcytic, normocytic and macrocytic categories.  Further consideration of
proliferation of marrow precursors,
  • the domination of a cell line, and
  • features of suppression of hematopoiesis

provide a two dimensional model.  Several other possible dimensions are created by consideration of

  • the maturity of the circulating cells.
The development of an evidence-based inference engine that can substantially interpret the data at hand and convert it in real time to a “knowledge-based opinion” may improve clinical problem solving by incorporating multiple complex clinical features as well as duration of onset into the model.
An example of a difficult area for clinical problem solving is found in the diagnosis of SIRS and associated sepsis.  SIRS (and associated sepsis) is a costly diagnosis in hospitalized patients.   Failure to diagnose sepsis in a timely manner creates a potential financial and safety hazard.  The early diagnosis of SIRS/sepsis is made by the application of defined criteria (temperature, heart rate, respiratory rate and WBC count) by the clinician.   The application of those clinical criteria, however, defines the condition after it has developed and has not provided a reliable method for the early diagnosis of SIRS.  The early diagnosis of SIRS may possibly be enhanced by the measurement of proteomic biomarkers, including transthyretin, C-reactive protein and procalcitonin.  Immature granulocyte (IG) measurement has been proposed as a more readily available indicator of the presence of
  • granulocyte precursors (left shift).
The use of such markers, obtained by automated systems in conjunction with innovative statistical modeling, may provide a mechanism to enhance workflow and decision making.
An accurate classification based on the multiplicity of available data can be provided by an innovative system that utilizes  the conjoined syndromic features of disparate data elements.  Such a system has the potential to facilitate both the workflow and the decision-making process with an anticipated reduction of medical error.
This study is only an extension of our approach to repairing a longstanding problem in the construction of the many-sided electronic medical record (EMR).  On the one hand, past history combined with the development of Diagnosis Related Groups (DRGs) in the 1980s have driven the technology development in the direction of “billing capture”, which has been a focus of epidemiological studies in health services research using data mining.

In a classic study carried out at Bell Laboratories, Didner found that information technologies reflect the view of the creators, not the users, and Front-to-Back Design (R Didner) is needed.  He expresses the view:

“Pre-printed forms are much more amenable to computer-based storage and processing, and would improve the efficiency with which the insurance carriers process this information.  However, pre-printed forms can have a rather severe downside. By providing pre-printed forms that a physician completes
to record the diagnostic questions asked,
  • as well as tests, and results,
  • the sequence of tests and questions,
might be altered from that which a physician would ordinarily follow.  This sequence change could improve outcomes in rare cases, but it is more likely to worsen outcomes. “

Decision Making in the Clinical Setting.   Robert S. Didner

 A well-documented problem in the medical profession is the level of effort dedicated to administration and paperwork necessitated by health insurers, HMOs and other parties (ref).  This effort is currently estimated at 50% of a typical physician’s practice activity.  Obviously this contributes to the high cost of medical care.  A key element in the cost/effort composition is the retranscription of clinical data after the point at which it is collected.  Costs would be reduced, and accuracy improved, if the clinical data could be captured directly at the point it is generated, in a form suitable for transmission to insurers, or machine transformable into other formats.  Such data capture, could also be used to improve the form and structure of how this information is viewed by physicians, and form a basis of a more comprehensive database linking clinical protocols to outcomes, that could improve the knowledge of this relationship, hence clinical outcomes.
 How we frame our expectations is so important that
  • it determines the data we collect to examine the process.
In the absence of data to support an assumed benefit, there is no proof of validity at whatever cost.   This has meaning for
  • hospital operations, for
  • nonhospital laboratory operations, for
  • companies in the diagnostic business, and
  • for planning of health systems.
In 1983, a vision for creating the EMR was introduced by Lawrence Weed and others.  This is expressed by McGowan and Winstead-Fry.
J J McGowan and P Winstead-Fry. Problem Knowledge Couplers: reengineering evidence-based medicine through interdisciplinary development, decision support, and research.
Bull Med Libr Assoc. 1999 October; 87(4): 462–470.   PMCID: PMC226622    Copyright notice

 

Example of Markov Decision Process (MDP) trans...

Example of Markov Decision Process (MDP) transition automaton (Photo credit: Wikipedia)

Control loop of a Markov Decision Process

Control loop of a Markov Decision Process (Photo credit: Wikipedia)

 

English: IBM's Watson computer, Yorktown Heigh...

English: IBM’s Watson computer, Yorktown Heights, NY (Photo credit: Wikipedia)

English: Increasing decision stakes and system...

English: Increasing decision stakes and systems uncertainties entail new problem solving strategies. Image based on a diagram by Funtowicz, S. and Ravetz, J. (1993) “Science for the post-normal age” Futures 25:735–55 (http://dx.doi.org/10.1016/0016-3287(93)90022-L). (Photo credit: Wikipedia)

 

 

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