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Greetings!
December 8th, 2010 by IatroCom1

Welcome to IatroBlog!

This web site is dedicated to:

Enabling A New Era in Healthcare Delivery

Information technology has increased consumer awareness and expectations for high quality healthcare. Well informed patients have created additional business requirements for their treating clinicians. Medical computing can serve the need for fast access to pertinent information anywhere and at anytime.

Facilitating the flow of information is key to improving management of healthcare resources. This is important to ensure industry-wide use of best practices and to achieve the consistent quality-of-care that informed consumers now demand.

Hand-held portable computers, like smart phones networked to the world-wide-web, with powerful medical softwares can rapidly link to a vast array of peer-reviewed and evidenced-based expert opinions that map to best practices, treatments and managment strategies to cost-effectively improve outcomes.

To be useful, computer tools should increase a doctor’s agility and competitiveness, improve productivity and provide fast return on investment at low risk. These utilities should not interfere with workflow routines. They should speed-up the pace of care provision and raise the level of patient satisfaction.

During the past 25 years, IatroCom has created and delivered such a mobile computing solution to help providers gain fast access to valuable decision-making information. We have deployed this application on web-enabled iPhone and iPad devices for easy use during encounter situations.

STATworkUP™ is a relational expert knowledge-base with a terminology framework designed to quickly collect, capture, correlate and analyze clinical data at the point-of-care. It’s unobtrusive architecture assists providers at what they customarily do to deliver appropriate evaluations. It does so in a modern and convenient way; plus it supports a wide variety of specialty disciplines as well.

Comprehensive assessment tools like these can serve to mitigate the risks of costly medical errors from omissions during  examinations. Additional benefits could also include the capture of encounter data at the point-of-service. Coupling such information gathering capability with secured electronic medical record repositories might also provide practice profiles to fiscal forecasters, for credentialing oversight regulatory affairs agencies, and to institutional medical policy makers. These devices could likewise help lower or avert the impact of doing costly unneeded procedures while perhaps suggesting more appropriate studies or treatments for various conditions and diagnoses. Such tools could be deployed to help establish patterns-of-care or perhaps determine medical necessity during claims processing.

This forum is a place to chat about these ideas and to offer feedback, suggestions and peer review about how the app is performing  in clinical situations. It is also a place to discuss ideas and offer advise that we might use to make the software even better.  We are interested in fostering conversations about best practices to help shape the field of clinical decision support using mobile computing devices as the field of healthcare informatics evolves.

This website has been certified by the HONcode and it abides by and follows the HONcode principles.

Platform moderators intervene daily by editing all postings to this Word Press blog site.  Users may be banned by not acting in accordance with the rules of this site.  The will not be warned or notified before being banned for infractions.

Qualifications and credentials of the the moderator:

Stephen G. Mlawsky, M.D., C.M.M., F.A.A.F.P.


C.E.O., Director, Founder, IatroCom Enterprises

contact information for this blog

ceo@iatrocom.org

The moderator is a volunteer with no conflicts of interest. Moderators are not paid as staff members of the company. Users and moderators must behave at all times with respect and honesty. Users are not allowed to post ads at this blog site. Users must post information which is true and correct to their knowledge.

The information provided on this site is designed to support, not replace, the relationship that exists between a patient/site visitor and his/her existing physician. All medical information presented must be attributed to an author and his/her training in the field must be mentioned.

Users are by default considered as non medical professionals. If the author is not a medical professional, this must be clearly stated on the web site.  All acronyms relating to degrees or affiliations must be explained.

Where appropriate, information that is posted on this site will need to be supported by clear references to source data and, where possible, have specific HTML links to that data. All sources of the medical content must be given.

You have to clearly indicate the recognized, scientific or official sources of health information quoted in your articles. If you used another website, a book, an article, a database or any other support, it has to be specified. The references specified have to lead us to the article mentioned. You have to provide a precise link to the source, whenever it is possible and the references should be in relation with the content referred.

Any claims relating to the benefits/performance of a specific treatment, commercial product or service will be supported by appropriate, balanced evidence. All information about the benefits or performance of any treatment (medical and/or surgical), commercial product or service are considered as claims. All claims have to be backed up with scientific evidence (medical journals, reports or others).  All brand names have to be identified (with ® for example). IatroCom receives no support for this Blog site.

Information posted in this blog will be recorded in the Iatroblog database hosted by WordPress.

Privacy policy:

IatroCom endeavors to honor or exceed the legal requirements of medical/health information privacy that apply in the United States. Confidentiality of data relating to individual patients and visitors to this medical/health Web site, including their identity, email addresses or/and contact information, names, personal or medical data posted in this forum will be edited to avoid sharing personally identifiable information over the Internet.

The sites does not display advertisements, other than for our own proprietary iPhone/iPad product named STATworkUP®.

Differentiation between it is presented herein to clearly delineate it as being original material created by the author of this blog and the IatroCom website.

The application is available for purchase at the iTunes Medical App Store.

Links to that site are posted the IatroCom web page :

http://www.statworkup.com

We encourage your comments and look forward to peer review and further discussions about ways to further improve the features of this or other related software to help improve the field of medical informatics.


2 Responses  
  • IatroCom1 writes:
    November 8th, 2010 at 4:48 pm

    Clinical Decision Support & Differential Diagnosis
    Using Mobile Computing Applications

    Abstract:

    IatroCom is a closely-held small company composed of doctors, computer engineers, and medical informatisists, founded by Dr Stephen Mlawsky. Our software development enterprises began in 1985 for the Macintosh computer. Throughout the years our applications became more sophisticated. In the 1990s IatroCom marketed, advertised and sold a product named MacON-CALL to doctors, nurses, and hospitals in 40 different countries around the world. Along the way the application was Internet enabled. With the advent of mobile devices, it was obvious that these could leverage our work. In 2010 we released a derivative app named STATworkUP. The delivery of integrated content on mobile devices, for use in clinical settings, just made good sense. We have built a very robust interface that is replete with great content in a nice and fairly clean user interface that is fully-relational. The objective has been to build and deploy a very useful clinical decision support application, full of good medical information, on intuitive and powerful devices like iPhones and iPads, to help busy providers do fast yet comprehensive problem assessments and to help improve care in the process of using it.

    A set of functionalities are important when mobile devices are used in health care:

    Decision support tools for fast yet comprehensive problem assessments
    Differential Diagnosis capability
    Practice Guidelines
    Advantages of web enablement

    STATworkUP

  • IatroCom1 writes:
    December 8th, 2010 at 4:34 pm

    Clinical Predictions:

    A clinical prediction rule is a type of medical research study in which researchers try to identify the best combination of medical signs, symptoms, and other findings in predicting the probability of a specific disease or outcome.

    Physicians have difficulty in estimating risks of diseases; frequently erroring towards overestimation, perhaps due to cognitive biases such as base rate fallacy in which the risk of an adverse outcome is exaggerated.

    In a prediction rule study, investigators identify a consecutive group of patients who are suspected of a having a specific disease or outcome. The investigators then compare the value of clinical findings available to the physician versus the results of more intensive testing or the results of delayed clinical follow up.

    These have had the consequences of their usage by physicians quantified.

    When studied, the impact of providing the information alone (for example, providing the calculated probability of disease) has been negative.

    However, when the prediction rule is implemented as part of a critical pathway, so that a hospital or clinic has procedures and policies established for how to manage patients identified as high or low risk of disease, the prediction rule has more impact on clinical outcomes.

    The more intensively the prediction rule is implemented the more benefit will occur.

    It is hoped that the use of STATworkUP may help to influence inclusion of prediction rules in practice. It is designed to correlate selected findings with a set of likely diagnoses based upon the probability settings.

    Where clinical prediction rules exist these are included in constructing the relationship between findings and diagnoses.


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