Category Archives: bReviews

Healthcare Big Data – Part 1

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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 >

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

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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).

6 Steps In Documenting Hospital Screening To Identify Patients With Nutritional Or Functional Needs

A hospital’s performance improvement activities as opportunities for improvement

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syringe-with-MCI.20.1-PI-activitiesIn three previous posts, I brought to you how aggregate data are an important part of the hospital’s performance improvement activities. In particular, the three posts were about aggregate data from risk management, utility system management, infection prevention and control, and utilisation review and how they can help the hospital understand its current performance and identify opportunities for improvement.

The posts were:

(i)                  JCI Standard MCI.20.1, ME 1 (Part 1) – risk management, in “The organization has a process to aggregate data in response to identified user needs.” ;

(ii)                JCI Standard MCI.20.1, ME 1 (Part 2) – infection prevention and control, in “The organization has a process to aggregate data in response to identified user needs.” ;

and

(iii)             JCI Standard MCI.20.1, ME 1 (Part 3) – utility system management and utilisation review, in “The organization has a process to aggregate data in response to identified user needs.”

Each of the links above will open in a new separate tab of your current browser window.

In this review of those 3 posts, I like to emphasise that a hospital chooses which clinical and managerial processes and outcomes are most important to monitor based on its mission patient needs and services provided. The hospital’s leaders must identify key measures (indicators) to monitor the hospitals’s clinical and managerial structures, processes and outcomes.

A required clinical monitoring which includes structure, process or outcomes data selected by the leaders is on aspects of infection control, surveillance and reporting. For managerial monitoring, a required managerial monitoring which includes structure, process or outcomes data selected by the leaders is on aspects of risk management and utilisation review/management.

The hospital collects and analyses aggregate data from clinical monitoring and managerial monitoring to support patient care and organisation management. Aggregate data provides a profile of the hospital over time and allows the comparison of the hospitals’s performance with other hospitals.

To measure the hospital’s performance improvement activities, hospitals usually prepare a master plan to reduce evident risks in the environment or individual plans which incorporates a comprehensive program and plan inclusive of :

  1. a program and plan to reduce the risk of health care-associated infections in patients, health care workers and visitors
  2. a program and plan that includes utility systems – electric, water and other utility systems,  maintained to minimise risk of failure

There is also a written plan for an organisation-wide quality improvement and patient safety program that includes clinical and managerial processes for risk management, utility system management, infection prevention and control, and utilisation review.

References:
Joint Commission International 2010, Joint Commission International Accreditation Standards For Hospitals, 4th edn, JCI, USA

Useful tips on medical records keeping from Medical Protection Society

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Image credit: nzppa.org.nz/

Medical Protection Society (MPS) was formed in 1892 after a number of high profile negligence and criminal cases in the United Kingdom made the headlines in the 1880s, and it soon became clear that individual doctors then did not have the resources to defend themselves in these cases. Today it is the world’s leading mutual medical defence organisation which has jurisdictions with members in more than 40 countries including in Malaysia, the UK, Ireland, South Africa, New Zealand, Hong Kong, Singapore, the Caribbean and Bermuda, and Kenya.

MPS aims to help doctors with legal problems that arise from their clinical practice. Members (doctors) in Malaysia seeking help with clinical negligence claims, complaints, legal and ethical dilemmas, and disciplinary procedures have access to expert advice from a team of medicolegal consultants based in Malaysia.

Health Information Management (HIM) / Medical Records (MR) practitioners in Malaysia can browse useful tips on medical records keeping from http://www.medicalprotection.org/malaysia/factsheets/Medical-records (this link will open in a new tab of your current broswer window) and more tips by exploring the MPS website at http://www.medicalprotection.org/malaysia/ (this link will open in a new tab of your current broswer window).

Big Data – Big Data Basics

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Big Data 3Vs cardboard-box-icon

This post is to continue from the introductory post Big Data – Introduction (this link will open in a new tab of your current browser window) on Big Data about the “3Vs” that define Big Data. As I researched the subject of Big Data, three terms – Volume, Velocity and Variety stood out in relation to the “3Vs” of Big Data which leads me to explain to you in this post the widely accepted definition of Big Data from Gartner (the world’s leading information technology research and advisory company) analyst Doug Laney who has characterised Big Data as “data that’s an order of magnitude greater than data you’re accustomed to.”

Accordingly, this “3Vs” model for describing Big Data spans three dimensions, data increasing in volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).

The first dimension/characteristic, Volume is about how Ed Dumbill, program chair for the O’Reilly Strata Conference (the leading event that offers the nuts-and-bolts of building a data-driven business – the latest on the skills, tools, and technologies you need to make data work and bringing together practitioners, researchers, IT leaders and entrepreneurs to discuss big data, Hadoop, analytics, visualisation and data markets –  the people and technology driving the data revolution), describes Big Data as “data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.”

To give you an idea of the volume of data that is increasing exponentially on an annual basis, customer transactions at Walmart is reported to estimate to more than 2.5 petabytes of data every hour. Perhaps these infographics, courtesy of the online storage site Mozy, and Cisco will help you visualise the meaning of pentabytes of data and how it expands further into zettabytes sometime into the future.

Visualizing The Pentabyte Age

Infographic credit : http://mozy.com/blog/misc/how-much-is-a-petabyte/

The Internet in 2015

Infographic credit : http://blogs.cisco.com/news/the-dawn-of-the-zettabyte-era-infographic/

Velocity, the second dimension/characteristic describes the frequency at which data is generated, captured and shared in every imaginable device that all produce torrents of data.

I am sure you have heard of a batch process that takes a chunk of data, submits a job to the server and waits for delivery of the result. In a batch process, the incoming data rate is slower than the batch processing rate but the result is useful despite the delay. For many new applications sources of data, the batch process is just not possible anymore since the speed of data creation is even more important  than the volume. The data is now real-time or nearly real-time  information streaming into the server in a continuous fashion.

The available data in the world today comes from everywhere, this Variety, the third dimension/characteristic signifies the proliferation of data types that add new data types  which no longer fits into neat, easy to consume structures of traditional transactional data, all of which exists as a by-product of ordinary  operations: those being generated by humans from posts to social media sites, digital pictures and videos, purchase transaction records, and GPS signals from cell phones, and from “sensor” data generated from computers and network devices and embedded chips used to gather climate information, from refrigerators and airplanes to bodily implants, and more.

The International Business Machines Corporation (IBM) adds Veracity as the fourth dimension of Big Data. Veracity is when the confidence of the quality (precision and accuracy) of the variety and number of information sources is doubted.

I guess this is enough to known briefly about the basics of Big Data.

References:
About 2012, O’Reilly Strata Conference, viewed 13 December 2012, < http://strataconf.com/strata2012/public/content/about >

Andrew, M & Erik, B 2012, Big Data: The Management Revolution, Harvard Business Review October 2012, Boston, MA, USA

Dave, F 2012, The 3 I’s Of Big Data, Forbes, viewed 13 December 2012,
< http://www.forbes.com/sites/davefeinleib/2012/07/09/the-3-is-of-big-data/ >

Diya, S 2012, The 3Vs that define Big Data, Data Science Central, viewed 13 December 2012, < http://www.datasciencecentral.com/forum/topics/the-3vs-that-define-big-data >

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 >

Stefan, S 2012, The 3 V of BIG Data, Agile Commerce, viewed 13 December 2012,
< http://multichannel-retailing.com/2012/05/the-3-v-of-big-data/ >

What is big data? 2012, International Business Machines Corporation (IBM), viewed 18 November 2012, < http://www-01.ibm.com/software/data/bigdata/ >