JCI Standard MCI.20.2 – Using or participating in external databases

Posted on 18/03/2013 by admin

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:

Hospital Compare which encourages hospitals to improve the quality of care they provide and for patients to find hospitals and compare the quality of their care and make decisions about which hospital will best meet their health care needs;
Quality Improvement Organization (QIO) – a private, mostly not-for-profit contractor of the Centers for Medicare & Medicaid Services (CMS) to improve the quality of health care for all Medicare beneficiaries;
ORYX® data reported on The Joint Commission website at Quality Check® which permits user comparisons of hospital performance at the state and national levels; and
hospitals complete The Leapfrog Hospital Survey, the gold standard for comparing hospitals’ performance on the national standards of safety, quality, and efficiency

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:

there is a process to participate in or to use information from external databases, thus satisfying the JCI Standard QPS.4.2, ME 2 which states that “Comparisons are made with similar organizations when possible.”;
the hospital contributes data or information to external databases in accordance with laws or regulations, thus satisfying for example both the JCI Standard PCI.10.4, ME 1 which states that “Health care–associated infection rates are compared to other organizations’ rates through comparative databases.” and the JCI Standard QPS.4.2, ME 2; and
the hospital compares its performance using external reference databases, also satisfying the JCI Standard QPS.4.2, ME 2; and the hospital maintains security and confidentiality when contributing to or using external databases.

References:

Facts about ORYX® for Hospitals (National Hospital Quality Measures), The Joint Commission, viewed 8 March 2013, < http://www.jointcommission.org/facts_about_oryx_for_hospitals/ >
Joint Commission International, 2010, Joint Commission International Accreditation Standards For Hospitals, 4th edn, JCI, USA
Prathibha, V (ed.) 2010, Medical quality management : theory and practice, 2nd edn, Jones and Bartlett Publishers, Sudbury, MA, USA
Quality Improvement Organizations, Centers for Medicare & Medicaid Services, viewed 6 March 2013, < http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityImprovementOrgs/index.html?redirect=/qualityimprovementorgs >
Welcome to the Leapfrog Hospital Survey, The Leapfroggroup, viewed 8 March 2013, < https://leapfroghospitalsurvey.org/ >

Disruptive innovation in health information management, Introductory post

Posted on 11/03/2013 by admin

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:

Technological enabler
Business model innovation
Value network

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:

Christensen, CM, Grossman, JH, and Hwang, J, 2009, The Innovator’s Prescription, A Disruptive Solution for Health Care, McGraw-Hill, New York, USA

Data Validation Process In Summary

Posted on 07/03/2013 by admin

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).

Data validation to ensure that good, useful data have been collected

Posted on 04/03/2013 by admin

Anyone who deals with data, will know that data is first acquired (collected) and verified (validated) before data input. Data input is then processed or managed which includes data storage, data classification, data update, and data computation. Data output is when the data input and processed or managed is retrieved and data is presented in a meaningful way.

Data acquisition (collection), data verification (validation), data classification, data storage, data update, data computation, data retrieval and data presentation are the eight elements which make up the three phases when we deal with data, that is the data input phase, the data management or processing phase and lastly, the data output phase.

Data are the raw materials that involves both the generation and the collection of accurate, timely, and relevant data through reliable measurements that ensures good, useful data have been collected.

Good, useful data involves using an internal data validation process in the authentication and validation of gathered data from authoritative, valid, and reliable data sources. It is important to consider applying the garbage in garbage out (GIGO) principle in collecting valid data.

If your hospital is implementing or has already begun a quality improvement program for example the Joint Commission International (JCI) hospital accreditation program, the quality of your hospital’s quality improvement program is only as valid as the data that you have collected through reliable measurements.

When using data for improvement and for establishing the level of confidence decision makers can have in the data when implementing or starting a quality improvement program, JCI (2011, pg. 156) recommends data validation in these following circumstances :

a new measure is implemented (in particular, those clinical measures that are intended to help an
hospital evaluate and improve an important clinical process or outcome);
data will be made public on the hospital’s Web site or in other ways;
a change has been made to an existing measure, such as the data collection tools have changed or the
data abstraction process or abstractor has changed;
the data resulting from an existing measure have changed in an unexplainable way;
the data source has changed, such as when part of the patient record has been turned into an electronic
format and thus the data source is now both electronic and paper; or
the subject of the data collection has changed, such as changes in average age of patients, comorbidities,
research protocol alterations, new practice guidelines implemented, or new technologies and treatment methodologies introduced.

JCI (2011, pg. 157) also recommends the following essential elements of a credible data validation process as an important tool for understanding the quality of the quality data:

re-collecting the data by a second person not involved in the original data collection
using a statistically valid sample of records, cases, and other data; a 100% sample would only be needed when the number of records, cases, or other data is very small
comparing the original data with the re-collected data
calculating the accuracy by dividing the number of data elements found to be the same by the total number of data elements and multiplying that total by 100. A 90% accuracy level is a good benchmark
when data elements are found not to be the same, noting the reasons (for example, unclear data definitions) and taking corrective action
collecting a new sample after all corrective actions have been implemented to ensure the actions resulted in the desired accuracy level

Health Information Management (HIM) / Medical Records (MR) practitioners do take note that 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 it must integrate data validation into its quality management and improvement processes, has an internal data validation process that includes (1) through (6) above, and the data validation process must include at least the measures selected as required in Standard QPS.3.1 when “The organization’s leaders identify key measures for each of the organization’s clinical structures, processes, and outcomes.” Such identified key measures is usually integrated as an ongoing standardised process to evaluate the quality and safety of the patient services provided by each medical staff member as required by the JCI Standard SQE.11 In other words, each of the hospital’s clinical structures, processes, and outcomes provided by each medical staff member are evaluated, and conclusions drawn from in-depth analysis of known complications of clinical structures, processes, and outcomes as applicable which are in turn used for all corrective actions to be implemented.

References:

Joint Commission International, 2010, Joint Commission International Accreditation Standards For Hospitals, 4th edn, JCI, USA
Joseph, T & Payton, FC, 2010, Adaptive health management information systems : concepts, cases, & practical applications, 3rd edn, Jones and Bartlett Publishers, Sudbury, MA, USA

6 steps in documenting hospital screening to identity patients with nutritional or functional needs

Posted on 24/02/2013 by admin

If you have been part or will be part of a Medical Records Review team at a hospital which is already Joint Commission International (JCI) accredited or seeking JCI accreditation status or undergoing re-survey for JCI accreditation status, you will be surprised how so many of the team members do not know the reason for nutritional screening which is the start of the the Nutrition Care Process – even among nurses in the team, and most will even not know where to find such evidence of nutritional screening in the medical record. Most of times, poor documentation in relation to the quality of nutrition documentation can be observed when nutritional screening data is not even gathered and forms left not filled appropriately.

In my opinion, it is the duty of the Medical Records Review team leader to highlight in his or her report non-compliance to nutritional screening among other findings, so that the hospital’s leaders can initiate a structured investigation to identify barriers to compliance for nutritional screening. I also strongly support that there must be an agreed standard for the type and context of screening tool(s) to be used, for example among a group of hospitals under an organisation. I believe standardisation facilitates research into barriers leading to poor documentation in relation to the quality of nutrition documentation, and this will lend credibility and usability of available screening tools for greater compliance.

Below is a diagram which summarises the steps in documenting hospital screening to identity patients with nutritional or functional needs, based on the previous post Hospital screening criteria data to identify patients with nutritional or functional needs (this link will open in a new tab of your current browser).