ICD 11 – The Content Model, Part 2

Posted on by
Reply

ICD 11 book coverIn this second part of the ICD 11 Content Model posts, I will aim to provide an insight into the basic structure of the model.

As you can read from the post ICD 11 – The Content Model, Part 1 (this link will open in a new tab of your current window), the revision process of the The 11th revision of the International Classification of Diseases and Related Health Problems (ICD ) is a broad participatory Webbased development process by the World Health Organisation (WHO).

This collaborative development of new content and proposed changes for ICD 11 is the responsibility of a Revision Steering Group (RSG) within the WHO ICD Revision Organisation Structure, which serves as the planning and steering authority in the update and the run-up to the revision process of ICD 11.

Today the Beta Draft of ICD 11 is available as the culmination of an information infrastructure and workflow processes started initially by Topic Advisory Groups (TAGs) for various specialty areas. The Webbased development of ICD 11 which is still open for comments and suggestions by interested parties in a social process on the Web, is integrated with knowledge of (i) diseases and health conditions, the eotiology and the anatomical and physiological aspects of the disease, (ii) input of all chapters and codes from existing clinical modifications of the ICD, and (iii) mappings to other terminologies and ontologies from other WHO-FIC (Family of International Classifications) members into computer systems, thus creating draft classifications for field testing as it is available in the Beta Draft of ICD 11.

I can prefigure the complex problems of developing ICD 11, which surely was undertaken and managed by using systematic approaches to deal with its development in a prescribed way and by using analytical techniques to identify and dissect the orderly arrangement of the mass of data already in a confused state into logical patterns thus promoting understanding and pointing the way to an appropriate decision within a clearly defined framework and a concrete context, the ICD 11 Content Model.

Thus, the Health Informatics and Modeling Topic Advisory Group (HIM-TAG) – also a part of the WHO ICD Revision Organisation Structure,  was entrusted to develop the ICD-11 Its task was to ensure that the Content Model remains the critical component of ICD 11 that specifies the structure and details of the information that should be maintained for each ICD category in the revision process.

The WHO (2013) describes the Content Model as a structured framework that captures the knowledge that underpins the definition of an ICD entity in the following ways:

  • includes the full scope of health care diseases and related health conditions (such as traditional medicine entries) so as to be as congruent with the overall structure 
  • ICD 11 entities are represented in a standard way from the currently set of different 13 defined dimensions or  main “parameters”, each parameter expressed using standard terminologies known as “value sets” by observing basic taxonomic and ontological principles including:
    1. key definitions: disease, disorder, syndrome, sign, symptom, trauma, external cause,
    2. separation of disability and joint use with the International Classification of Functioning, Disability, and Health (ICF),

    3. attributes  – etiology, pathophysiology, intervention response, genetic base, and

    4. linkages to other classifications and ontologies, including that of for Primary Care, Clinical Care and Research
  • the Content Model enables content experts to view and curate i.e to pull together and sift through and select for presentation its contents using software tools that allows automatic error checking and enforces constraint enforcement thus maintaining the correctness or validity of the stored data (integrity ). 

Each category ICD 11 entity in the Content Model will be described by 13 different, defined dimensions or main “parameters” as can be seen below.

ICD-11-content-model

More in the next post on the ICD 11 Content Model.

References:

  1. World Health Organisation, 2012, Content Model, viewed 18 March 2013, < http://www.who.int/classifications/icd/revision/contentmodel/en/index.html >

Healthcare Big Data – Part 1

Posted on by
Reply

Big Data 3Vs cardboard-box-iconTo continue from from the introductory post Big Data – Introduction (this link will open in a new tab of your current browser window) and Big Data – Big Data Basics (this link will open in a new tab of your current browser window), this first part will introduce the subject of big data in healthcare and end there.

As you would surely be aware even as a Health Information Management (HIM) / Medical Records (MR) practitioner from your practice of managing medical records that an individual patient’s clinical signs and symptoms, medical and family history, and data from laboratory and imaging evaluation found in his or her medical record is used by the attending doctor to diagnose and then treat the patient’s illnesses. This traditional clinical diagnosis and management approach to treatment has been and still is often a reactive approach, i.e., the doctor starts treatment/medication after the signs and symptoms appear.

However given the genetic variability between individuals and advances in medical genetics and human genetics eversince the Human genome project completed in 2003, medical genetics and human genetics have since provided both scientists and clinicians to understand health and manage disease, that is to say that it has been providing a more detailed understanding of the roles of genes in normal human development and physiology and the risk for many common diseases, not in the same way diseases have been understood in the traditional reactive approach.

Standard test data – of an individual patient’s clinical signs and symptoms, medical and family history, patient discharges, real-time clinical transactions and data from laboratory and imaging evaluation found in his or her medical record, and data in personalised medicine or PM when medical decisions, practices, and/or products are tailored or customised to the individual patient with the use of genetic information (genomic data) – from the study of biological data of the complete set of DNA within a single cell of an organism of the individual, or a combination of the two, creates vast collections of data – Healthcare Big Data..

Healthcare Big Data has tremendous potential to add value from analysing and mining these vast collections of data now available to hospitals in general.

But Healthcare Big Data must be managed, leveraged and integrated to help personalise care (as in PM), engage patients, reduce variability and costs, and ultimately improve quality.

In order to manage, leverage and integrate Healthcare Big Data, Big Data solutions are needed to transform health care with big data. Big Data solutions apply analytics to examine, better analyse and understand the large amounts of data of unstructured clinical data in the form of images, scanned documents, and encounter or progress notes in its native state, integrate it with operational structured data based on historical and current trends, to uncover whatever hidden patterns, unknown correlations and other useful information, and then they help predict what might occur in the future with a trusted level of greater reliability. Healthcare Big Data analytics is all useful information because such information can provide competitive advantages over rival hospital organisations and result in business benefits, for example more effective marketing and thus generate increased revenue.

In the next post on Healthcare Big Data, I shall be blogging about the challenges in aggregating the Healthcare Big Data from multiple sources.

References:

  1. Denise, A 2013, Leveraging big data analytics to improve healthcare delivery, ZDNet,  viewed 30 March 2013, < http://www.zdnet.com/leveraging-big-data-analytics-to-improve-healthcare-delivery-7000013072/ >
  2. Geoffrey, SG and Huntington, FW (eds.) 2010, Essentials of Genomic and Personalized Medicine, Academic Press, Elsevier Inc, San Diego, CA, USA
  3. Lorraine, F, Michele, O’C,  & Victoria, W 2012, Data, Bigger Outcomes, American Health Information Management Association, viewed 18 November 2012, < http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049741.hcsp?dDocName=bok1_049741 >
  4. Margaret, R, 2012,  DEFINITION big data analytics, TechTarget, viewed 1 April 2013, < http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics >
  5. Neil, V 2013, Big Data Use In Healthcare Needs Governance, Education, InformationWeek, viewed 30 March 2013, < http://www.informationweek.com/healthcare/clinical-systems/big-data-use-in-healthcare-needs-governa/240151395 >

2016: The Year of the Zettabyte

Posted on by
4

My last post on Big Data was way back February 3, 2013! This weekend I hope to continue on Big Data posts and post it by early next week,

However this evening I stumbled upon a new infographic related to my Big Data post posted February 3, 2013. In that post I wrote about the volume of data that is increasing exponentially on an annual basis and to give you an idea of how that is developing in two infographics, courtesy of the online storage site Mozy, and Cisco to help you visualise the meaning of pentabytes of data and how it expands further into zettabytes sometime into the future.

Well, by end of 2016, the world according to Cisco’s Visual Networking Index, will cross the Zettabyte threshold largely contributed by video streaming, phone lines or video calling and mobile streaming accelerated via extremely fast Internet speeds and data transfers.

The following infographic is a visual of how big zettabytes will be.

INFOGRAPHIC - 2016 - The Year Of The Zettabyte V6

References:

  1. XO Communications, Are you Ready for 2016: The Year of the Zettabyte, viewed 28 March 2013, <http://www.xo.com/services/Pages/2016-The-Year-of-the-Zettabyte.aspx>

Findings revealing workarounds to overcome design flaws in electronic health records (EHRs)

Posted on by
Reply

A new study in the Journal of the American Medical Informatics Association (JAMIA), which directly observed clinical workflows at primary care clinics in different healthcare organisations in Boston and Indianapolis, USA found that both doctors and medical staff used both paper-based and computer-based workarounds to overcome design flaws in their electronic health records (EHRs).

Here are some examples from the study’s findings of workarounds created when practices are found not using the EHR in the way it was designed for, due to the real and perceived deficiencies ot the EHRs.

workarounds-for-EHR-flaws

References:

  1. Ken, T, 2013, Healthcare Workarounds Expose EHR Flaws, InformationWeek Healthcare, viewed 26 March 2013, < http://www.informationweek.com/healthcare/electronic-medical-records/healthcare-workarounds-expose-ehr-flaws/240151710 >

The need for discharge planning and discharge planning documentation

Posted on by
Reply

The attending doctor is responsible for a patient’s care and determines the patient’s readiness for discharge based on the policies and relevant criteria or indications of referral and discharge established by the hospital policy guiding the referral or discharge of patients .

Referring or discharging a patient to a health care practitioner outside the hospital, another care setting, home, or family is based on the patient’s health status and need for continuing care or services.

Continuity of care requires special preparation and considerations for some patients, such as for discharge planning.

Discharge Planning is a process which is initiated as soon as possible upon inpatient admission, that is during the initial assessment which includes determining the need for patients for whom discharge planning is critical due to age, lack of mobility, continuing medical and nursing needs, or assistance with activities of daily living, among others.

The discharge planning process includes a mechanism to identify those patients for whom discharge planning is critical. A discharge planning worksheet is generated based on a list of criteria and used as an assessment tool by a case manager or an utilisation manager (if there is one at your hospital, or in most instances initiated by a nurse), to identify patients who may require post-hospital services on discharge for inpatients once their acute phase of illness has passed. This worksheet is used to develop the Case Management Note which is a progress note documented by the case manager or an utilisation manager (if there is one at your hospital, or in most instances by a nurse),which outlines a discharge plan that includes case management/social services provided and patient education.

Discharge planning involves discussions on discharge plans with patients and their families on admission and during the hospital stay. A discharge plan is prepared to help determine home needs, assist in planning for needed medical equipment, helps in choosing a facility for care if the patient is unable to return home, and facilitates discharge to home or transfer to another facility.

The Case Management Note is not the same document as the Discharge Note which is the final progress note documented by the attending doctor, which includes details like the patient’s discharge destination (e.g., home), discharge medications, activity level allowed, and follow-up plan (e.g., office appointment).

Health Information Management (HIM) / Medical Records (MR) practitioners do take note that Health Information Management / Medical Records Management services does not include Discharge Planning. However HIM / MR practitioners can expect to find a Case Management Note included in some patients’ medical records.

HIM / MR practitioners who are members of a closed Medical Record Review, need to be aware that the Medical Record Review Tool will assess and determine the degree of compliance with standards and elements of performance relating to discharge planning given to some patients as required by the Joint Commission International  (JCI) Standard AOP.1,11 which states that “The initial assessment includes determining the need for discharge planning.”, if you are working at a hospital which is already JCI accredited or seeking JCI accreditation status or undergoing re-survey for JCI accreditation status.

I like to point out that the Medical Record Review Tool has an error that shows the JCI Standard AOP.1.8.1 (Early screening for discharge planning) as found in the JCI Hospital Survey Process Guide, 3rd Edition, Effective January 2008 instead of showing the JCI Standard AOP.1,11 with regards to compliance in discharge planning. You can find my corrected version of this JCI recommended Medical Record Review Tool from this link (the form will open in a new tab of your current window).

References:

  1. Joint Commission International, 2010, Joint Commission International Accreditation Standards For Hospitals, 4th edn, JCI, USA
  2. Michelle, AG & Mary, JB 2011, Essentials of Health Information Management: Principles and Practices, 2nd edn, Delmar, Cengage Learning, NY, USA