Poland will implement a comprehensive web-based electronic patient
record (EPR) project which is in its beginning phase and a comprehensive introduction will be available starting in 2014. Read more about this project from this link (this link will open in a new tab in your current browser). This press release from CompuGroup Medical AG, Poland was received via email courtesy of Arunasalam P., Malaysia.
The Content Model of an ICD entity in the 11th revision of the International Classification of Diseases and Related Health Problems (ICD) forms the basis of this succeeding post to the earlier post ICD 10 & ICD 11 Development – How, What, Why & When (this link will open in a new tab of your current window).
It is not my intention to write volumes on Content Model, rather I shall attempt to share the basics of this model in its simplest form that I have understood as compared to ICD 10.
We know that ICD 10 had evolved to include morbidity classification from its original design to record causes of death. We are aware that ICD is also used for reimbursement (in countries like in the US), and also used in specialty areas such as oncology and primary care.
Then we also know that from the ICD-10 tabular list found in Volume 1, ICD 10 is organised as a monohierarchy. Monohierarchy is a top-down classification. Perhaps the following example of a monohierarchy among Felidae, the biological family of the cats will make things clearer of what I wish to write about how ICD 10 codes are organised.
ICD 10 uses letters for an initial broad categorisation (e.g., I for diseases of the circulatory system) and combined with digits (e.g. I00 to I02) for each successive level of child codes. Sibling codes (e.g. I01.0 and I01.1) are considered to be exhaustive and mutually exclusive, requiring the use of residual categories—“unspecified” and “other”—at each level, (e.g. I01.9 Acute rheumatic heart disease, unspecified).
A code may have associated inclusions (I10 Essential (primary) hypertension Incl: High blood pressure) and exclusions (e.g. I01.0, Excl: when not specified as rheumatic [I130.-]).
Inclusions are exemplary terms or phrases that are synonymous with the title of the code or terms representing more specific conditions (e.g. I21 Acute myocardial infarction Incl.:myocardial infarction specified as acute or with a stated duration of 4 weeks (28 days) or less from onset).
Most exclusions are either conditions that might be thought to be children of a given condition but, because they occur elsewhere in the classification, must be excluded from appearing under it (e.g. I25.2 for old myocardial infarction); others are codes representing possible co-occurring conditions that should be distinguished from the condition (e.g.I23 Certain current complications following acute myocardial infarction i.e to say co-occuring or concurrent with acute myocardial infarction (I21-I22).
As I have posted in the posts ICD 11 – history of the development of the ICD from 1853 to 2015 (this link will open in a new tab of your current window), ICD 11 is been developed as a participatory Web-based process.
The development of ICD-11 is aimed to create an information infrastructure and workflow processes that utilises knowledge from existing hierarchies of codes and titles found in ICD 10 Volume 1 as I have elaborated above, and supplementary volumes of rules (found in ICD 10 Volume 2) and indices (found in ICD 10 Volume 3).
This new ICD 11 information infrastructure captures the knowledge that underpins the definition of an ICD entity as we know of it today – again as I have elaborated above, which will thus aid the review of best scientific evidences to enable the definition of diseases and health conditions, encoding of the eotiology and the anatomical and physiological aspects of the disease, and mappings to other terminologies and ontologies.
Initially the workflow of the collaborative development of new content and proposed changes, review and approval processes, and the creation of draft classifications for field testing was undertaken by Topic Advisory Groups (TAGs) for various specialty areas.
The workflow continued with the Alpha Draft of ICD-11 revision process with comments and suggestions by interested parties collected in a social process on the Web and ended by May 2010, and continued with the Beta Draft with field trials of draft standards.
The Alpha and Beta drafts have produced the new ICD 11 information infrastructure based on the Content Model for ICD 11 which represents ICD entities in a standard way, each ICD entity defined by “parameters” representing different dimensions – a parameter expressed using standard terminologies known as “value sets” that specifies the structure and details of the information that should be maintained for each ICD category in the revision process and which thus allows for computerisation.
In the next post, I shall post about the basic structure of the Content Model.
References:
In order to compare its performance and to identify opportunities for improvement, a Hospital needs a mechanism for comparing its performance to that of other similar hospitals locally, nationally, and internationally with recognised, internationally accepted standards.
The mechanism must be designed to transform input forces and movement by (i) operate or interact by participating in external performance databases, (ii) compare its performance to that of other similar hospitals, into a desired set of output forces and movement when the hospital can identify opportunities for improvement and hence documenting its performance level.
This arrangement of connected parts in a system of parts of individual hospital performances like those parts of a machine is surely an effective tool to demonstrate the quality and safety that are being provided in the hospital and can be thought of as benchmarks of success when the hospital participates through reference databases.
I can think of the following initiatives in the US when hospitals as providers participate through reference databases to improve by benchmarking their performance against others, encourage private insurers and public programs to reward quality and efficiency, and help patients make informed choices:
In all instances, hospitals need to check if they are required by local laws or regulations to contribute to some external databases. Hospitals also need to maintain security and confidentiality of data and information at all times when operating or interacting with external databases.
ff your hospital is a hospital which is already JCI accredited or seeking JCI accreditation status or undergoing re-survey for JCI accreditation status, then the JCI Standard MCI.20.2 requires it to have a mechanism in place with the following characteristics:
References:
Way back to my post The Innovator’s Prescription by Clay Christensen, a early review of this book (this link will open in a new tab of your current window) dated 20 May 2012, I introduced to you this book. I finished reading this book some months back and kept on hold posts on it until now.
I shall not be writing a book review nor a book summary but I have decided to illustrate how enablers of disruptive innovation specifically in health information management can combine to transform the very expensive care from highly trained professionals into one that is much more affordable, accessible, of better quality and simple.
The agent of transformation is disruptive innovation. It consists of three elements:
The diagram below shows these elements with regulatory reforms and new industry standards facilitating or lubricating interactions among these enablers in the new disruptive healthcare industry.
I shall end this post at this point, with more posts to come.
References:
Measurement is about selecting what is to be measured, selecting and testing the
measure, collecting the data, validating the data, and using the data for improvement.
Validating the data is an important tool for understanding the quality of the quality data which is reliable, accurate, and defensible data that has been validated, for establishing the level of confidence decision makers can have in using data and in their implications for clinical practice.
An example of performance measurement is when an area for improvement in structure, process, and/or outcome is identified, new guidelines for patient care and safety are usually developed by the hospital using the data which had been selected, tested, collected, validated for patient care and safety improvement. This change process is normally managed by the hospital and include key stakeholders (e.g., clinicians) affected by the change.
An example of data validation when Health Information Management (HIM) / Medical Records (MR) practitioners who are generally specialised or experts is in disease coding may be involved, is when they provide advice in disease coding validation studies to determine staff training needs.
To ensure that a sample is valid when evaluating performance, it is critical to always determine an appropriate sample size ie. the number of subjects to choose, a procedure to ensure that your sample is representative of the population i.e the degree to which the subjects are similar to those in the intended use, and also determine the types of data to be used (administrative or clinical).
Well, you need to sample so as to try to get one that represents the population as closely as possible. This is because we rarely have enough time and money to look at the entire group of people that we are interested in (for example, the population of everyone attending a clinic at a particular hospital).
In trying to getting a valid sample, let us assume you had limited money, you cannot
study the target population as a whole. By all means do select a small sample size but when you choose a small sample size, there is always a higher risk of sampling error being present, for example when you could only choose only two patients out of the population of 30.
Unclear data definitions and inconsistent coding of data are reasons when data elements are found not to be the same. It is vital to have a list of codes with their definitions that you are going to be using throughout the collection of data. For example, if you are coding ward clerk as 1 and charge nurse as 2, it is important to ensure that you have used the same codes throughout the process of entering data into the dataset. In data validation, It is important to make corrective actions when inconsistent coding of data is found. However, if you do decide to change some data codes, it would be wise to note any changes as you progress.
The chart below characterises the process of data validation (by clicking on the chart below, it will open in a new tab of your current window, and by clicking on the image in this new tab, you can view a larger view of the chart).
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