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Department of Health

Clinical Decision Support System Report

Advice for Victorian Public Health Services

OCIO Health Design Authority

Victorian Health Design Forum Report – Clinical Decision Support FINAL v1.0.doc

6 Jun 2013

Health Design Forum Report

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Document Control:

Name Victorian Health Design Forum Report – Clinical Decision Support Systems

Date 6 Jun 2013 Status: FINAL

Version 1.0

Authors Health Design Authority

Health Design Forum members Reviewers

Approvals This document template has the following approvals:

Name Title Signature Date of Issue

Version

Health Design Forum

Version Amendment History Version Author Date Nature of Amendment

0.1 OCIO HDA 1 May 2013 Document creation

0.2 OCIO HDA 15 May 2013 Document revision following internal review

1.0 OCIO HDA 6 Jun 2013 Final for release

© Copyright, State of Victoria, Department of Health, 2013

All rights reserved. No part of this publication may be reprinted, reproduced, stored in a retrieval system or transmitted, in any form or by any means, without the prior permission in writing from the Department of Health (DH)

In addition, the contents may not be disclosed to any person, association or company unless employed by DH directly or in a contracting and consulting capacity

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Table of Contents 1. Executive Summary .................................. ............................................................. 4

2. Document Overview.................................. ............................................................. 6

2.1 PURPOSE........................................................................................................................................ 6 2.2 SCOPE............................................................................................................................................ 6 2.3 ASSUMPTIONS ................................................................................................................................. 7 2.4 INTENDED AUDIENCE ....................................................................................................................... 7 2.5 CDSS INTRODUCTION ..................................................................................................................... 7

3. Background and Current State ....................... .................................................... 10

3.1 BACKGROUND ............................................................................................................................... 10 3.2 CONTEXT ...................................................................................................................................... 11 3.3 ANALYSIS...................................................................................................................................... 12 3.4 POTENTIAL BENEFITS AND DRAWBACKS OF CDSS.......................................................................... 20 3.5 CLINICAL DECISION SUPPORT SYSTEM CURRENT STATE ................................................................. 21 3.6 CLINICAL DECISION SUPPORT SYSTEM LESSONS LEARNT ............................................................... 24

4. Clinical Decision Support Systems Options.......... ............................................ 25

4.1 OPTION 1 – INTERFACED SYSTEM .................................................................................................. 26 4.2 OPTION 2 – INTEGRATED SYSTEM .................................................................................................. 28 4.3 REFERENCES ................................................................................................................................ 32 4.4 GLOSSARY .................................................................................................................................... 33

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1. Executive Summary “Up to 4000 Australians die every year as a result of diagnostic errors and a further 21,000 Australian patients would experience “serious harm”. The diagnostic errors resulted from system problems, such as glitches in communication pathways between health providers and cognitive human error, including settling on the first available diagnosis” 18

This paper explores the various tools or systems available to assist health professionals in their quest for a proven “real-time” Clinical Decision Support (CDS) system to offer improved care to patients and reduce the risk of errors and lower the disease burden in Victoria. This paper reviews the systems and their maturity levels, it assesses their abilities and lists many leaders and experts in the field. The types of Clinical Decision Support Systems (CDSS) explored and analysed can be categorised into two groups:

1. Integrated:

• Electronic Medical Records (EMR) with or without analytics (encapsulated Business Intelligence)

2. Interfaced:

• Business Intelligence (BI)

• Inference engine

• Master Data Management (MDM)

• and machine learning systems.

This paper identifies that to achieve true CDS interoperability amongst the VPHS the definition and adoption of a common national information model or framework for CDS would be of great value and this concept of a common CDS information model would eventually juxtapose into a virtual medical record for the State.

Further, it is the conclusion of this report that Electronic Medical Records (EMR) applications with encapsulated analytics (Business Intelligence) in combination with all or partly with a BI, Inference engine, or machine learning tool would deliver the greatest benefit if framed within HIMSS EMRAM level 5 or above, as this defines the internationally acceptable level of CDS capabilities. Further, with this injection of supporting technology, health organisations could provide the ability to successfully analyse, plan, forecast and strategise better ways to offer patient care.

Where there are communities of health organisations wanting to share data whether it be hospital, community or research data, then the same recommendations apply. A shared EMR with encapsulated analytics or a BI system running across campuses in conjunction with an inference engine or machine learning system would be of the greatest benefit to the community. Conversely the governance structures, standardisation and interoperability become even more important as the need to capture data flowing from multiple resources needs the correct context and strong data management. It is also however recognised that there are significant limits to adopting this type of information sharing in the current community environment. As noted by the “Continuity of Care” HDF paper, there are significant barriers and the roadmap to this future outcome is lengthy.

This report has found that there are systems such as MDM and machine learning that lack widespread usage, or maturity. They may also be either experimental or development stages and have not quite hit the plateau of success. These systems are explored further and formulate part of this paper’s analysis.

This paper concludes that:

1. The use of EMRs have already demonstrated their value in operational decision support including areas such as error detection, contraindication relating to medication and diagnostic ordering, result flags, allergy related alerts etc.

2. There are specialised systems that enable specialists in the particular area to use a very specific CDS system to achieve superior results for their patients, in their context e.g. inference engine for diabetes care.

3. Predictive analysis is becoming more commonly mentioned in the literature, which tends to imply greater take-up. Again the operational penetration of predictive analysis is for point

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solutions rather than holistically. Predictive analysis seems to be an area where significant growth maybe expected in the medium term.

4. Expert systems, whether these be inference engines, artificial intelligence solutions (Watson), or take another form are in their developmental stages and are being used in health more as a curiosity, i.e. proving the concept and its potential value to the health sector, than for regular operational purposes.

5. CDS at a population level is of interest, especially for emergency scenarios such as a major disease outbreak, though typically the necessary interoperability and information exchanges are not in place to facilitate.

The dependencies for effective CDS within health organisations are not dissimilar from those mentioned in other HDF papers:

1. Standards and the enforcement of standards are essential to having a common library of data that can be used for various CDS purposes, from alerting to diagnostic predictive analysis to treatment or condition management pathways.

a. Governance across all elements of CDS

b. Data standards

c. Nomenclature standards

d. Mapping standards between nomenclatures

e. Standards for clinical documents that encapsulate patient information relevant to CDS

2. Data capture. Information must be captured in order for it to be useful in any CDS context

3. Interoperability. The ability to accumulate information from disparate systems and to return CDS outcomes to the source systems is essential to success. Interoperability is of course supported by standards.

Whatever direction health organisations gravitate towards, the human element or human analysis plays a large role in the success of CDS systems with one aspect of caring for a patient that cannot be captured by a machine which is the physical examination of a patient using the human senses such as sight, scent and touch. I.e. pedal pulse palpation, breast lump examination or diagnosing infection. The technology drives data intelligence, but humans drive the intelligent interpretation of the data.

The prevailing evidence suggests that EMRs with the ability to make intelligent inferences and systems such as machine learning tools and mature MDM engines are the way of the future, although not mature at this stage they are the tools with the greatest expectation. As pressures mount on these systems to improve, so will the need to store increasing amounts of data, such as genomic or research data in the hope to achieve a more personalised and pseudo “medicine model that could transform healthcare systems and catalyse significant reductions in morbidity, mortality and the overall healthcare costs”.20

“The ability to exchange information between clinical information systems without loss of clinical meaning is essential to enable safe and effective implementations of automated decision support. Whether a decision support system requests specific information from an EHR system, or an EHR system requests specific computations from a decision support system (and both of these pattern of interaction are used), it is essential that the clinical information exchanged is understood accurately and consistently by both systems” 3

This report acknowledges the lack of integration generally to support CDSS in current clinical and EMR systems across the Victorian Public Health Sector (VPHS). This is an area where change is expected in the medium to long term. Standard integration is required to achieve a high level of CDS.

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2. Document Overview 2.1 Purpose This document provides advice and guidance to Victorian Public Health Sector (VPHS) health services under the auspices of the Health Design Forum. The HDF has been established to provide independent and expert guidance to the Victorian health sector on a range of business solutions, trends, and IM&ICT projects.

The subject of this report is Clinical Decision Support Systems (CDSS).

2.2 Scope This scope of this document includes:

• Consideration of CDSS for Victorian public health services and their staff

• An overview of the CDSS

• Current state description of CDSS

• Local, national and international experiences and trends

• Available CDSS options each with an assessment (strengths, weaknesses, opportunities, threats)

• A clear definition of CDS, and CDSS

• The models available in, or relevant to, the Australian healthcare sector to support CDSS.

• Options for health organisations in the implementation of proven CDSS models

• Provide broad types of CDSS leaders in the field and their relevant experience

• Evidence of CDSS benefits and the impacts to health organisations

• Exploration of local, national and internationally experience

• Integration standards associated with CDSS

• Potential pitfalls when defining rules and models

• Responsibility for clinical safety - i.e. what is the boundary between clinical safety on a national level or system level vs. within the health care service?

• How and who explores the interfaces between CDS and community practices - i.e. care pathways. What does that mean for community providers and how would it work?

• Business Intelligence frameworks and tools - where do they fit?

• Governance of clinical decision support terminology and rules

• Clinical governance of best practice in decision support, and determination of rules that are agreed by clinicians as pragmatic, consistent and clinically safe.

• CDS effectiveness in a distributed environment, e.g. community care/GP/PCEHR?

• Where does CDS fit within national infrastructure?

• Structure and approaches around content - feasibility of sharing content and how and when do hospitals share data within a health organisation?

• Trends in CDA and CDSS looking both locally and internationally.

This document excludes:

• Detailed designs for CDSS solutions for use within specific health services.

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2.3 Assumptions The following assumptions have been made in the preparation of this document:

1. Readers have a basic understanding of the Victorian Public Health Systems (VPHS) environments and have an appreciation of ICT in health.

2.4 Intended Audience The intended audience of this document includes:

• Victorian Department of Health Office of the Chief Information Officer (OCIO) Design Authority & Integration Services staff

• Victorian Department of Health OCIO management

• Victorian public health services

• Other interested parties

2.5 CDSS Introduction 2.5.1 CDSS Purpose The main purpose of CDS systems is to support a range of clinical functions including and not limited to the following examples: clinical coding and documentation, managing clinical complexity, keeping patients on research databases and maintaining protocols, tracking orders, referral follow-up, developing preventative care programs, monitoring medication orders by avoiding duplicate or unnecessary tests, supporting clinical diagnosis and treatment plans while promoting use of best practices, population-based management and more.

It is intended that clinicians would interact with CDS tools to help determine diagnosis, prepare treatment plans and understand data patterns. The clinician consumes the information and decides what the diagnosis really is or may order future tests based on the information provided by the CDSS to determine or rule out possible diagnoses. Post diagnosis CDSS systems are used to mine data to derive data patterns, past medical history, clinical research to predict future events.

2.5.2 Definitions CDSS in an electronic medium and in “real-time” can be described as determining diagnosis, clinical interpretation, trending, alerting or predictive analysis of patient data that involves invoking an application (or interface or service), providing it with data, and having it invoke some action like alerting a clinician of a duplicate order or that a patient has a particular allergy to a medication being considered for treatment by either a passive or more active means. I.e. looking up MIMS (passive) or ordering a medication such as Morphine and the EMR application providing an alert at point of care that the patient has an allergy to Morphine (active) Robert Hayward of the Centre for Health Evidence suggests “Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care, such as artificial intelligence.”

Alternatively, another definition suggests the CDS provides clinicians, patients or individuals with knowledge and person specific or population information, intelligently filtered or presented at appropriate times, to provide faster more efficient health processes, better individual patient care, and better population health.

CDSS encompasses a variety of tools and evidence based medicine that assist healthcare providers in providing safe and effective care for patients. The use of CDSS tools take a variety of forms, including flowsheets, assessment instruments, chronic disease management, genetic risk assessment tools, drug interaction tools and rules based alerts to name a few.

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2.5.3 CDS and BI Maturity Models Illustration 1 depicts the five levels of implementation of a BI system (or similar) that has all the elements required to implement a successful CDSS according to Gartner.

Illustration 1 - Gartner BI Maturity Model

Essentially the diagram above commences with no capability to integrate business intelligence to significantly improved interoperability with full integration of a BI tool or other that has the capability to provide CDS “real-time”. An example of a maturity level 5 described by Gartner could look like a clinician ordering morphine for a patient while receiving a clinical allergy that the patient has an allergy to morphine. This model or the newly launched model by HIMMS are useful yardsticks to measure your health organisation CDS capability.

HIMSS Analytics is launching a model in the first half of 2013 to assist health organisations to benchmark their clinical and business intelligence tools, which maybe of material assistance to VPHS when they consider CDS implementation. HIMSS will assist organisations in understanding the effectiveness of the tool and whether the tool is integrated with the business side of the hospital while ensuring the impact to physicians is positive and seemless.12

The objective is to improve CDS of patient care, transparent diagnoses and improved treatments, etc.

The governance burden potentially increases with each increase in maturity, as is consistent in many other disciplines, and as greater reliance is placed in the CDS systems.

Based on the diagram above, available literature, the Capability Maturity Model Integration (CMMI), and the Office of the Chief Information Office (OCIO) analysis a possible CDS system maturity model is proposed:

1. Simple alerting and similar information implemented across single environment - allergies, meds, duplicate orders checking etc.

2. Complex alerting across multiple systems, but effectively doing the same thing as point # 1 above.

3. Provision for diagnoses, symptoms, orders and results

4. Treatment support, whereby the CDS provides treatment pathways that have proven effectiveness for patients with the same condition(s) and psycho-social profile.

a. Note the general lack of effective quality / outcome measures in this space.

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5. Items 3 and 4 but leveraging population data, thereby improving the reliability of the CDS suggestions.

6. Population trend and event analysis, so in a bird flu outbreak (possibly in another region) and people present with typical symptoms, the clinician can be alerted.

7. Artificial intelligence, where the system can leverage imbedded information, but also learns from experience. (optional)

8. Input to local, strategic and population health initiatives based on good data analysis and a comprehensive CDS.

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3. Background and Current State 3.1 Background The Health Information Management System Society (HIMSS) EMR adoption model encourages health organisations to not only migrate the paper medical record to an electronic version, as early as stage two in the seven step process that defines a health organisation’s EMR capability at an international level. It pushes the boundaries by taking the information that is entered about a patient to be useful and meaningful to clinicians that can convert to well informed clinical decisions. CDS systems store patient information, have intelligent and sophisticated configuration capabilities and rule based methodologies to guide or provide protocols that ultimately improve patient care. This in turn assists health organisations to better understand the demographic each health organisation is dealing with and to formulate health strategies.

CDS is a broad topic that includes areas such as:

• clinical documentation

o vital signs, flow sheets, nursing notes, clinical hand-over

• discharge summaries

• care plans

• error checking

• orders entry

• medications

• protocols

• standards

• health law,

• results

• and above all clinical care.

The focus of this paper will be more geared towards providing information about CDS systems and how they can be applied rather than focus on internal health processes or procedures. It is recognised there is a cross over.

As early as late 1960, F.T deDombal and his associates at the University of Leeds studied the diagnostic process and developed computer-based decision aids using Bayesian probability theory. By 1971 the Leeds Abdominal Pain System went operational, and found fantastic levels of success where the CDSS produced a correct diagnosis of 91.8% of cases compared to the clinician who rated 79.6% accurate. Using the Bayes’ theorem, the probability of seven possible explanations for acute abdominal pain (appendicitis, diverticulitis, perforated ulcer, cholecycstitis, small-bowel obstruction, pancreatitis and non-specific abdominal pain) was used5. Profoundly, the computer system accurately diagnosed appendicitis with no cases of failure.

Most CDS systems consist of three parts, the knowledge base, the inference engine, and a mechanism to communicate. The knowledge base includes all the rules, configuration and in some cases will contain a database the inference engine combines the rules from the knowledge base with the patient data and the communication tool provides the ability to output the data into meaningful information for clinicians. Illustration 2 depicts a typical enterprise data warehouse architecture.

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Illustration 2 - Enterprise Data Warehouse Architec ture

Systems that do not use a knowledge base use a form of artificial intelligence called machine learning. Machine learning devices learn from the patterns in the data, for example if a group of clinicians order the same course of antibiotics for a cohort of patients and of the cohort, none of the patients’ recorded improvements in their symptoms, (providing the data is entered) the learning system would alert clinicians that the antibiotic selected may not be an effective treatment for patients of this nature, whereas a rules based tool will not consider the effectiveness of the medications being prescribed.

3.2 Context In the interest of reducing the disease burden the following statistics from the Australian Bureau of Statistics (ABS) has listed the top long-term health conditions facing Australians as [18]:

• Arthritis 14.8% 3.3 million people

• Mental and behavioural conditions 13.6% 3.0 million people

• Asthma 10.2% 2.3 million people

• Heart Disease 4.7% 1.0 million people

• Cancer 1.5% 326,600 people

• Diabetes 4.0% 875,400 people

In addition, the Victorian Metropolitan Health Plan Technical Paper predicts annual increases in the following areas of healthcare [16]:

• Emergency and Medical 2.8%

• Chemotherapy 4.0%

• Renal and Peritoneal 4.8%

• Palliative Care 2.7%

• Mental Health 2.0%

The impact to health organisations is complex as early detection and preventative medicine strategies are formulated to reduce the disease burden and to accommodate the changes occurring in the health industry. CDS systems have the ability to improve medical knowledge, provide in-depth analysis and consolidate cohorts of data that will be at the finger tips of clinicians and researchers. This information

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when used effectively could assist the overall burden of disease. This paper will endeavour to explore how this may be achieved.

3.3 Analysis There are at least five defined types of CDS methodologies. They are as follows:

• Bayesian Network: shows a set of probabilistic relationships between diseases and symptoms based on conditions. It uses a variety of sources to call upon to assist in the overall diagnosis of a patient and is widely used in the area of paediatric neurology.

• Neural Network: Artificial Intelligence System that uses machine learning logic. Copious amounts of data need to be feed into the neural network system with local, state, national and international knowledge to assist with patient care. It recognises statistical patterns over time.

• Rules Based System: A set of rules are input into the system and alerts pop-up as a result of something the clinician has touched. PBS eligibility is an example of applying a set of rules to the configuration of an application and based on the rules combined with the patient data coming through, a certain criteria will be met to enable the patient to be either eligible or ineligible for PBS.

• Logical Condition: Is a simplistic logic that searches for data that within certain limits. If the data breaches those limitations then a flag is generated to warn the clinicians. I.e. haemoglobin level is abnormal based on a set of criteria feed into the system.

• Causal Probabilistic Network: Uses symptoms, states and diseases. Relates data to disease classifications using best fit logic.

Here are some examples of the CDS systems that have been implemented based on but not limited to the methodologies listed above.

• CADUCEUS/ INTERNIST – is a rules-based expert system designed at the University of Pittsburgh in 1974 for the diagnosis of complex problems in general internal medicine

• SimulConsult – A clinician can simulate a consultation and conduct a differential diagnosis or relative probability to isolate the distinct diagnosis. The expert system uses text book analytics against entered data, results and observations to narrow the diagnosis/disease or genome. This system is effective in paediatrics and genetics. The system contains a combination of text books and medical journals. This material links to the current case as it is being described. (http://www.simulconsult.com)

• DiagnosisPro is a differential diagnosis reminder tool that offers diagnosis and is used for labs, ECG, x-ray, CT-scan, MRI, Ultrasound, pathology and microbiology results and more. Detailed information with more than 7000 diseases with a disease comparison tool to compare two diseases side by side. Clinical protocols can be used as an educational tool.

• DXplain – uses modified Bayesian logic, but provides a list of ranked diagnoses associated with the symptoms. DXplain provides justification for why each of these diseases might be worth considering, suggests what further clinical information would be useful to collect for each disease, and lists what clinical manifestations, if any, would be unusual or atypical for each of the specific diseases.

• MYCIN/EMYCIN– is a rules based system that helps identify types of bacteria causing an infection. I.e. meningitis. It has had further developments to handle infectious diseases.

• ONCOCIN subsequently developed after MYCIN , which is a rules based system developed in 1980s created to reduce the number of clinical trials and now is used widely for the treatment of cancer. The system was developed mainly for residents and clinical assistants. The system has been hampered by length of time to input a new protocol; it can take up to six weeks.

• RODIA (Relative Optical Density Image Analysis) is a methodology and software solution for relative optical image analysis used in medical imaging, diagnostics, orthopaedic and other medical disciplines. RODIA is also used in telemedicine as a remote clinical decision system for physicians. The system provides evidence-based medicine and statistical evaluation of collected data.

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• CASNET (Causal Associational NETworks) developed in the 1960s, was a general tool for building expert systems for the diagnosis and treatment of diseases. This system is famous for diagnosing glaucoma. A drawback can be the expense of implementing this sort of system.

• PIP, the (Present Illness Program), was a system built in England in 1970 that gathered data and generated hypotheses about disease processes in patients with renal disease.

• QMR – Quick Medical Reference. A diagnostic decision-support system with a knowledge base of diseases, diagnoses, findings, diseases associations and lab information. With information from the primary medical literature on almost 700 diseases and more than 5,000 symptoms, signs and labs

• ABEL (Acid-Base and Electrolyte program). An expert system employed causal reasoning, for the management of electrolyte and acid base derangements. Developed at the laboratory of Computer Science, MIT, in early 1980s.

• Electronic Medical Record (EMR) systems provide comprehensive clinical decision support for clinicians based on configuration, scripting and rules that assist clinicians to better support patient care. Alerts, reminders and pop-ups are integrated into EMRs to validate the patient’s five rights, correct patient, right drug, correct dose, correct route, right time. For example vendors including Cerner, Epic, Intersystems, Micromedex etc build this functionality into their clinical systems for this purpose.

• VERISK Health has a variety of enterprise analytical solutions such as: DxCG Intelligence, Enterprise Intelligence, Medical Intelligence and Provider Intelligence. These types of systems help manage vendor performance, population health, health trends, clinical decision support, enterprise reporting, budgeting, network efficiency, risk contracting and accountable care initiatives. The systems can codify data from many sources into a common warehouse, letting you deliver consistent, accurate results across your enterprise.

• WATSON is a machine learning system that uses the neural network methodology. WATSON has the capacity to analyse copious amounts of data, it can draw differential diagnosis or disease matching equations while learning about the data as it consumes it. Watson can also be used for cohort data analytics, population statistics and patient care.

• 3M GROUPER is a rules and inference type tool used in relation to case mix classifications and ICD10 coding. The system has been used in health organisations nationally and internationally for years. The sophisticated system groups patient data into clinically meaningful categories based on primary and secondary diagnosis data derived by clinicians using hospital systems.

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3.3.1 Gartner Hype cycle (Adaptation)

Illustration 3 - Hype Cycle (Adaptation of Gartner Hype cycle) 13

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The various CDS system types can be described and critiqued using the Gartner hype cycle (previous page), where an explanation of the system or tool will be rated in terms of its maturity level and described to present further information to illicit their adaptability in the current environment. This hype cycle diagram illustrated by Gartner11 has some interesting highlights. From the illustration the left curve articulates the expectation of technology, this is known as the hype curve, it usually has great expectations by individuals or health organisations that quite often exceed delivery of the technology.

To put this into context, we can examine the Healthcare-Assistive Robots which has a great deal of hype surrounding it. There are few products truly available to deliver this type of nursing care that could replace a nurse, however there are a few reported success stories beginning to emerge. Recently, it was reported that a man that was clinically dead for 40 minutes, “has been brought back to life by an Australian-first resuscitation technique.” The Alfred are trialling a mechanical Cardiac Procedure Response (CPR) machine, which performs constant chest compressions, and a portable heart-lung machine – normally used in theatre – to keep oxygen and blood flowing to the patient’s brain and vital organs.13

3.3.2 Inference Engine

Illustration 4 - Inference Engine

The inference engine or artificial intelligence framework draws conclusions based on information logically applied. The Bayesian type model bases logic / decisions on probabilistic matching techniques. It can also use simple rules-based logic, mappings, configuration, code sets and protocols as a basis to provide information to the user. It draws deductions on “if x, then y” and “if….

Inference engines take into account the rules, mapping, configuration, protocols, standards and standardisation and provide probable diagnosis, disease, treatment etc. It does not learn from the material. There are some really mature vertical inference applications, however they are still disparate from the overall EMR history and often require separate logins and often have no linkages with an EMR or Database. Often clinicians are typing in symptoms rather than the information linking to the inference model.

An example of an inference engine includes Simulconsult and DXplain.

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3.3.3 Machine Learning The machine learning tools have the capacity to learn from the data available. The core of machine learning deals with representation and generalisation. The system works on statistical patterns in the data it draws on patient data, research material, medical journals, health organisational protocols, standards and standardisation, health law and code sets that are all feed into the machine learning enterprise.

Illustration 5 - Machine Learning

LAB

RespiratoryCardiolab OtherGP’s

RAD

MEDS

DS

BOS

Allergy

Alerts

Other

Probabilistic Matching Portal

D iagnostics

Users - EMR

Machine Learning

Standards, Protocols, Code Sets, Medical Journals, incidents, White papers

RULESMA PPING

CONFIGURATION

One or many health organisation

Knowledge Base

Machine learning uses probabilistic and causal probabilistic matching techniques that can understand patient symptoms it will suggest probable diagnosis or suggest further diagnostics to determine a disease or condition. The machine learning system is only as clever as the information that is feed into it.

This technology has been implemented sporadically across the health sector internationally. At a local level the design authority is not aware of any Australian health organisations using machine learning technology such as Watson. The machine learning tool does not specifically have interoperability with an EMR, although it can query the information to draw conclusions.

It may have limitations around access to information as it generally does not have a knowledge base in the true sense of the word.

The Watson product from IBM is an example of a machine learning tool.

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3.3.4 Master Data Management At the top of the hype curve depicted on illustration 1 sits the Master Data Management (MDM) technology system, where health organisations would benefit enormously, however MDM has not yet proven itself in this space.

Illustration 6 - Master Data Management

This system is an advanced performance analytical enterprise data warehouse that offers substantial new investments to leverage clinical data on business initiatives to coordinate care among previously siloed entities to provide a “single version of truth”. MDM has sadly fallen flat in the delivery space especially in health, however it remains a well sort after methodology to be able to unite a group of health organisations, cleanse the data, apply a set of rules, create mappings and apply configuration, then to seamlessly open a portal to have a broad set of CDS that is at the clinicians fingertips and is designed specifically for an organisation/s. This technology could shape the future providing it can link in with an EMR.

Oracle Sun JCAPS can produce this type of simulation, however mostly banks are producing good results in this area. I.e. ANZ are using this currently for customer data.

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3.3.5 Business Intelligence

Figure 7 - Business Intelligence

The Business Intelligence model is broadly used amongst health organisations. The system operates on rules and mappings, it has a level of sophistication by taking into account standards and protocols and code sets. The clinical decision support really comes into place when the data that is stored in the warehouse is manipulated or extracted from the system. This is widely used by health organisations within the VPHS.

Clinicians and hospital administrators can then analyse the data to meet a variety of reporting requirements.

Examples include products from Oracle, Elsevier and Accenture.

3.3.6 Electronic Medical Records (EMR & EHR) Coming down from the hype cycle and delivering good outcomes in the CDS system space are the EMR applications. Systems from vendors such as Cerner, Intersystems, CMS (BOSSNet), and CSC are delivering CDS systems to the VPHS. These systems are reporting good benefits in the areas of medication management, orders and results for areas such as pathology, radiology and other diagnostics, nursing notes and clinical documentation. See illustration 10 for statistical benefits reported about mature implementations of EMR systems according to HIMSS.

Moving more towards the technologies and usable catalogues ICD10 and SNOMED CT are heading towards the “plateau of productivity” status; while content management, natural-language processing and HL7 messaging have a very penetrating and mature status already according, to Gartner. 13

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Illustration 8 - EMR with Analytics

Figure 9 - EMR with Separate Analytics

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3.4 Potential Benefits and Drawbacks of CDSS The first CDSS was developed more than 40 years ago and although the concept has developed over time, it appears the technology as a holistic approach still has a long way from maturing into the system/s that meet health organisation expectations.15 The systems were designed to improve patient safety, through reduced medication errors and adverse events and improved medication and test ordering. CDS systems have the potential to deliver improved quality of care, increased clinician access to patient histories, increase clinical pathways and guidelines, facilitating up-to-date clinical evidence, improved documentation and patient satisfaction. They have the potential to improve efficiencies, reduce the amount of unnecessary test duplication, and change in patterns of drug prescribing favouring cheaper but equally effective generic brands. Further, CDS systems can support education and training, use of protocols, ease of extracting research data and creating standard and hospital reports.

With the expected benefits come some potential drawbacks. The threat that is explored in this paper is in the clinical judgement area, whereby clinicians can rely on a “machine based technology” to do work that is still the clinician’s responsibility to make the clinical judgement with the information provided. Quite often systems are more expensive than expected, with high maintenance costs, support and training. If systems are not running in parallel then clinicians could find it too time consuming to work with multiple systems. This translates to multiple logins and there can be over reliance on the system that suppresses the clinician’s ability to diagnose a patient. 15

Further, a study from the Netherlands aimed at improving survival after a myocardial infarction, found that cardiologists and nurses have preconceived ideas about using “inference engine” type CDS systems. The study analysed the perceived barriers to CDS systems amongst nurses and cardiologist to realise some of the obstacles of implementing a CDS system. The study found that on responsibility and trust, the study found that 65 percent of respondents said they believe the “inference engine” type CDS systems can make mistakes. Ninety percent stated that advice of a CDS should always be checked. Seventy-nine percent stated that they are responsible for the treatment of “their” patients and not the CDS. Eighty percent, however, said they believe the CDS offers helpful advice.19

The following benefits of an EMR CDS have been derived by HIMSS analytics. 15

Illustration 10 - Benefits of CDSS

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Illustration 10 displays respondents who are clinicians who marked a reduction in turn around times as the most beneficial benefit of implementing an EMR. To be able to have “real-time” data is the greatest benefit noticed by the health sector when implementing an EMR and moving away from paper records. Improved drug ordering and administration times was next, followed by a decrease in cost of paper, improving charges, decrease in transcription costs, reduction in lab cost, reduction in antibiotic start times and general improved documentation quality. For further information about benefits realisation, please refer to the design authority website: http://www.health.vic.gov.au/designauthority/forum.htm

3.5 Clinical Decision Support System Current State 3.5.1 Victoria The Victorian Public Health Sector (VPHS) has embarked on many projects related to CDS systems that ultimately improved patient outcomes. There has been significant work in the implementation of Electronic Medical Record (EMR) systems for hospitals, community systems and the like, to record patient vitals, patient and family history, create orders, records results and, provide discharge summaries, add allergies and medications etc, Some applications have their own extracting tools to extract the data for data mining analytics. The VPHS has wide use of BI tools for mining data, there are a range of adapter tools to help analyse the patient data if an EMR does not have the capacity to produce analytics. The majority of health organisations have implemented data warehouses or Business Intelligence (BI) systems that enable data mining or research analytics. The BI tools have improved dramatically over the years, however this report has found that analytics is commonly completed after the patient has gone home. The following tools are currently in use within the VPHS:

• IBM Cognos

• Cyberquery

• Oracle, who have a suite of data mining and business intelligence tools

• SAS Enterprise Miner

• IBM SPSS Modeller

• Angoss Knowledge Studio

• Elsevier / Medai

• SAP Business Objects

• Others.

Under a Department of Health and Ageing (DOHA) project in October 2012 RadLogix was successful in obtaining a funding grant under the Diagnostic Imaging Quality Program (DIQP) to conduct a project with the aim of investigating and making recommendations on the implementation challenges for a Diagnostic Imaging (electronic) Clinical Decision Support and Order Entry System used in GP practices.

The Health Design Authority, Department of Health has spent years delivering standards and guides for the VPHS which not only supports interoperability within the state in the current environment, but positions health organisations to adopt national initiatives, like the Personally Controlled Electronic Health Record (PCEHR) and although there is limited CDS within the PCEHR itself, the National initiative will provide patient data that can improve CDS even if the data is separated.

It has been noted by NEHTA that the work that Victoria has undertaken over the past five years through the Office of the Chief Information Officer has provided an excellent foundation for better national interoperability.

Some of these standards and guides include:

• The OCIO Unified Guide (HL7 standard)

• Pathology and Radiology catalogues

• Solution design and architecture documentation

• Medications catalogue and design principles localised to the Victorian health care environment.

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More information on NEHTA and Victorian standards can be found at:

• www.nehta.gov.au

• http://www.medicareaustralia.gov.au/provider/vendors/index.jsp

• www.standards.org.au

• www.health.vic.gov.au/hdss

• www.health.vic.gov.au/designauthority

3.5.2 Australia The body responsible for the take up of ehealth in Australia is NEHTA and Standards Australia and they have a variety of projects on the agenda to improve CDS. These projects include:

• Rapid Integration Projects (RIP) across all jurisdictions which involves the implementation and integration of Clinical Documentation Architecture (CDA) compliant discharge summaries into the Personally Controlled Electronic Health Record (PCEHR), the Individual Healthcare Identifiers (IHI) that provide a unique and national patient identifier, the Healthcare Provider Identifiers-Individual (HPI-I) and Healthcare Provider Identifier-Organisations that all link to the communication of the overarching Personally Controlled Health Record (PCEHR),

• eReferral project(s),

• Child eHealth Record based on the NSW Health ebluebook solution

• Individual agencies are planning for the future, especially in areas such as adoption of the IHI, medical practitioners’ HPI-Is and health services’ HPI-O, and the PCEHR in hope that one day that no matter where a patient presents, a patient record is accessible to ultimately improve patient outcomes. Alongside the recommendations of the National E-Health strategy are projects to align code sets at the National level, catalogues such as the Australian Medicines Terminology (AMT), National orders, Medical Benefits Scheme (MBS) & Pharmaceutical Benefit Scheme (PBS), National Health Data Dictionary, Health Level 7, and finer grained standards for expressing clinical information have been developed as standardized data types (ISO 21090), terminologies (SNOMED CT) and nomenclatures (ISO 11073). 3

• HL7 V2 Virtual Medical Record Profile

• Standards-based Clinical Decision Support for Australian Primary Care

• Integration Specification for Australian Point of Care Systems to interoperate with independent Clinical Decision Support Services.

A great deal of the national agenda will be driven by standardisation, standard code sets, and standard messaging layers.

3.5.3 International International literature is reporting exciting advances using data analytics platforms. Recently, researches at Washington University in St. Louis, have used a complex and powerful algorithm developed by Brown University to assemble the most complete genetic profile yet of acute myeloid leukaemia. The study was based on many mutations that contribute to the pathogenesis of acute myeloid leukaemia (AML). These are largely undefined currently. The relationships between patterns of mutations and epigenetic phenotypes were not clear. In the conclusion it was stated “that we identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients.” The information and databases from the study are available.

In another example, Harvard researchers began using “a big data analytics platform to build computational models to analyse and explore how various medications react. This project attracted a 3.75 million grant from the National Cancer Institute to store thousands of cell samples in the cloud. The American Society of Clinical Oncology meanwhile is planning to create a huge database track, in real

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time, the treatment effectiveness for thousands of patients. The proto-type of the system, known as the CancerLinQ network, is expected to provide “second opinions times multiples”. 10

IBM Watson™, named after IBM founder Thomas J. Watson, was built by a team of IBM scientists who set out to engineer a computerised system that could exceed a clinician’s ability to store, recall and analyse medical data after their success in the financial market.

It essentially has artificial intelligence that can not only store and understand medical journals, copious amounts of results, and patient notes but it can also help provide potential diagnosis, identify further tests that need to be carried out before a diagnosis can be reached. Watson also has the ability to learn and keep on learning. It should be used as a source of useful information for clinicians to use as an assistant rather than being relied upon to solely diagnose patients. The machine learning technology allows Watson to be current at all times as it is constantly feed with information daily. Watson can be useful for analysing population statistics, which maybe useful when considering the burden of disease statistics.

According to IBM, “The real innovation will come when care providers begin to employ analytics to mine patient data correlated with family histories to provide better risk assessment, prediction and prevention, earlier detection; earlier treatment, and targeted treatments. Or, when healthcare practitioners can access unique and actionable insights from the massive amounts of data constantly being generated in medical research, clinical knowledge and journals to help treat an individual patient.”

The Watson tool could be useful at almost any level including locally, state, nationally and internationally. IBM Watson supports predictive analysis which is the main strength of the tool, to be able to predict when an event could occur by using patterns of data may change the way clinicians work in the future. It should be seen as a tool to assist clinicians and health administrators rather than a replacement commodity. It is early stages for this revolutionary tool that has started to be used in the United States. Watson requires localisation, it requires key standards and protocols, and it requires a great deal of data to be useful. IBM Watson does not come with this type of information already built into the system, it relies on being feed constantly with information and the right type of information. If things have changed or protocols don’t exist any longer the Watson system will need to be kept up-to-date. Possibly this is a drawback of health organisations taking up such a revolutionary tool, because the system is high in maintenance.

Figure 11 - IBM Watson Architecture

As mentioned in this paper HIMSS Analytics is launching a model in the first half of 2013 to assist health organisations to benchmark their clinical and business intelligence tools, which maybe of material assistance to VPHS when they consider CDS implementation.12

There are also syndromic surveillance systems which are used for studying infectious diseases by many large and small organisations such as the World Health Organisation (WHO) and Centre for Disease Control (CDC) and Prevention and of course there are many individual disease registries from stroke and chronic conditions such as diabetes, cancer and domestic violence to name a few.

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In Durham, North Carolina, there are reports that to achieve CDS nationally there needs to be a “National Clinical Decision Support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis and therapy. The model looks at standardizing patient data across health information systems, centrally managed repositories of computer-processable, medical knowledge and standardised approaches for applying these knowledge resources against patient data to generate and deliver patient specific care recommendations.” 20

3.6 Clinical Decision Support System Lessons Learnt Clinicians have a duty of care and are responsible for patient care, a draw back of the artificial intelligence model is that it will not hold up to accountability and sometimes they are not reliable.

From the information sourced in this paper most research articles suggested to be aware of the integration pitfalls and to ensure integration compliments the clinical workflow, rather than clinicians having to login to multiple screens to obtain patient data in a fragmented way. Electronic data for all information is useful while paper is not as useful. If using an inference engine then the data needs to be instant for clinicians and therefore clinicians are not waiting until the patient has discharged.

“A 2005 systematic review by Garg et al of 100 studies concluded that CDSS improve practitioner performance in 64% of the studies. The CDSS improved patient outcomes in 13% of the studies.” 4

3.6.1 Technical Challenges With any CDS system there are bound to be challenges in designing and deploying suitable products that suit your organisational need, amongst those challenges is integration. In many health organisations the composition of applications is varied to say the least. The data received can be varied and often the rules and configuration are different. This makes it challenging for health organisations to draw data into a system and rationalise the data in a clinical setting. This can be attributed to the complexity of the data, the rules, mapping and configuration required to add clinical decision support to the mix.

Health systems are profoundly complicated and often are not connected in any way or simply just have different rules. Consistent workflow can become problematic across campuses; therefore sharing data has its limitations.

Should an organisation implement an inference engine model that requires additional log-ins, this could cause concerns amongst clinicians and may result in poor uptake.

When new systems are selected it is easy to configure a system to alert and provide pop-ups and flags, however it is essential the system doesn’t create an unwieldy amount of alerts, otherwise clinicians will simply ignore them.

When providing an inference engine system, such as Watson with copious amounts of data it is important that conflicting arguments be recognised that may cause discrepancies with patient care. This is where clinicians need to weigh in against a machine that is providing information that may not make sense from a clinical point of view.

3.6.2 Barriers Varonen et al. identified potential barriers and inhibitors when general physicians used CDS systems. Varonen et al recorded they (clinicians) experienced dysfunctional computer systems, potential harm to the doctor-patient relationship, unclear responsibilities, threats to clinicians’ autonomy, and extra workload due to excessive reminders, however poor computer skills can also be a barrier. The next generation may however be more computer literate and more willing to use CDS systems and if this is the case they may produce better outcomes. On the other hand, a “study earlier this year from the University of Missouri showed that most patients took a dim view of doctors who make use of clinical decision support technology”, as it can appear they are still using training wheels rather than being a competent capable clinician who is interested in double checking their own rationale or diagnoses.12

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4. Clinical Decision Support Systems Options

The CDS systems have been grouped into two categories, firstly the interface category which includes HL7 messaging, APIs, extracts flowing inbound and outbound of a system that can provide CDS, however the solution may be disconnected from CDS view point. This category includes:

• Inference Engine

• Machine Learning

• Master Data Management (MDM)

• Business Intelligence.

Secondly, the category that is integrated has less messaging and is likely to offer a more complete CDS solution, such as an EMR or clinical system. The variance within this solution is the analytics component that may or may not separate the CDS solution. This category includes an EMR, EHR or Clinical systems with analytics/reporting.

Both interfaced and integrated options are becoming more advanced and are more equipped to be able to interrogate multiple systems in multiple health services. In the future healthcare potentially could have a virtual medical record to share between health organisations locally, state, national or internationally. To do this effectively, further alignment of standards, codes sets, data quality, interoperability protocols and alignment of terminology would be needed to achieve this level of interoperability together with a strict governance structure.

Conversely, “the real problem with interoperability is not standards. We’ve got more standards than we can deal with. The problem is the buyers of healthcare – the hospitals (health organisations), the big healthcare organisations, the integrated delivery networks. Once they see it’s in their interest to demand that the vendors be interoperable, that will change things. But so far, they haven’t done that. 2

This analysis identifies that to achieve true interoperability amongst the VPHS the definition and adoption of a common information model for CDS would be of great value and this concept of a common CDS information model would probably fit the definition of a virtual medical record (vMR). A common CDS information model is needed in order to enable the design, development, and deployment of scalable and interoperable CDSS should health go down this path. A distinction should be made between common applications as opposed to a common CDS information model.

Further, the governance, compliance and execution of any such system or model would need careful planning.

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4.1 Option 1 – Interfaced System Option one is the interfaced system model that has the ability to store data and can either manually or automatically analyse clinical data for CDSS outside the EMR application. Rules and configuration can still be applied to this model, however some of these models are standalone point to point systems that lack interoperability and continuity of care.

The systems explored under the interface umbrella include:

• Machine learning

• Inference engine

• Business Intelligence

• Master Data Management.

The machine learning tool, is a standalone tool that can work along side a clinical or EMR system by consuming copious amounts of data (protocols, standards, results, patient data, medical journals and trusted medical information) and derives clinical assumptions based on the information provided, like patient diagnosis. The machine learning system can understand data and can compute when drug interactions are not effective for cohorts of patients or can understand when there is a potential break-out of infectious disease for example.

The main strengths are:

• A form of validation of diseases, care plans, medication management for interns, residents, registrars, doctors and nurses

• Can be used as a training tool to simulate patient scenarios

• The system learns from the data and can display sophisticated inferences based on the information provided, if there are patterns of data emerging the system will determine correct or suggest testing based on the information.

• The system can retain so much data that it would be impossible for a human to retain

• The system is always current.

The main weaknesses are:

• Clinicians may fall into the trap of trusting a system rather than exercising their own clinical or medical expertise

• Dependency issue

• A system can only be as good as the data it is feed. If the data is unreliable then the system will reflect this behaviour.

• High cost ++

• High resource dependency to input data

• High integration component.

The inference engine is similar to the machine learning tool, however it can not learn from the data that it is exposed to. It is rules based and can provide probability equations to assist clinics with diagnosis, treatment plans and appropriate testing.


• A form of validation of diseases, care plans, medication management for interns, residents, registrars, doctors and nurses

• Can be used as a training tool to simulate patient scenarios

• Can be an expert in a vertical medical field

• Can be used as a training tool to simulate patient scenarios.


• Integration limited

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• Separate to clinical or EMRs (usually)

• Usually siloed by clinical expertise. I.e. A microbiology system is quite specific to a particular clinical area, likewise RODIA is a system for optical density imaging, therefore excellent tools for a vertical medical specialty.

o Note: this could also be viewed as a strength.

The BI tool which is the most widely used tool, consumes patient data, it can understand configuration, mapping, standardisation and rules. The output of data can be presented using extracts, reports or portals.


• Robust

• Proven methodology

• CDS Analytics can be extracted, reported or viewed

• Capacity to store various data sources.


• Can still be a manual process of extracts reports and viewing data

• Disjointed from EMR (although BI can send and receive data if configured)

• Often data reports, extracts are not real-time

• May be some inconsistency in obtaining extracts or mining data depending on security levels or user profiles, and the nature of the data. BI tools generally need IT personnel to provide extracts, reports etc. The real limitation with these products can be associated with general health workers not having access to write their own extracts or reports when needed and are usually forced down a more formal process to obtain simple information.

The MDM tool is a widely desirable model, it understands the same rules and configuration associated with BI, however the MDM tool can sync data from multiple sources (across multiple facilities or campuses) and can query the data from multiple sources, apply cleansing rules before storing it in a database for presentation either by delay or instantaneously, either by a portal or other form as a viewer. This type of model is used by banks and financial institutions, however is not proven in the health industry.


• The system is ideal for organisations with large connecting health communities. (I.e. hospital, community system, GPs etc)

• Data cleansing beneficial before extracts, reports or portals are viewed

• System capable of high configuration, rules and multiple nomenclatures

• Proven with financial institutions

• Flexible

• Technical advanced and sophisticated

• Mapping exercise of multiple sources are extremely difficult and unwieldy (I.e. Mapping AMT to MIMS).


• High risk – largely unproven in health

• High maintenance, configuration, mapping and rules

• Lack of resources with the knowledge to achieve MDM quality

• Clinical information is so variable and so changeable; MDM is not equipped specifically for health.

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4.1.1 Option 1 Strengths and Weaknesses - Overall

Strengths

• Flexible and configurable

• Supports and validates diagnosis, treatment plans for clinicians

• Improves CDS

• Provides differential diagnosis and probability matching

• There are specific products that match an organisation. Such as paediatric medicine, genetics, cancer etc

• Statistical analysis of population cohorts to reduce disease burden and improve patient care.

• The system can trend, find patterns of data to draw conclusions

• The non-interfaced options have the ability to use quite large libraries of text books and medical journals

• Can be a learning platform for clinicians as they move through organisation to organisation

Weaknesses

• May be too complex and hence difficult to implement and manage

• Governance if not managed can cause problems

• Would need to be proven, perhaps through beta testing

• Clinicians could become too reliant it. The focus is patient care

• Manual connection of data or disconnect of data can be problematic

• The products are only as good as the information it is provided and the way it is configured

• May not provide an integrated view with key systems, such as an EMR (Not providing immediate feedback)

Opportunities

• Learn from international experience and experts

Threats

• To be risky in health may put patient’s lives at risk. The use of known and proven products usually achieves the best outcomes.

4.2 Option 2 – Integrated System The EMR option in its entirety is a fully integrated system. This paper acknowledges the disparity between speciality systems and the fact there needs to be some integration, although EMR solutions are data accumulators and are able to perform CDS quite well proving the right level of application usage is purchased.

The EMR core can be described as a “black box” of information. The EMR draws information about patients from key inputs, then it applies clinical algorithms, rules and configuration that live within the EMR system that can draw on knowledge such as SNOMED CT, Medline, LOINC, AMT etc.

Illustration 8 presents the interconnectedness of an EMR system and although there is some interfacing, the system is relatively contained within an EMR where there are alerts, warnings or confirmations to underpin clinical decisions without having to log into multiple systems. Most EMR products have their own analytics tools that come as a packaged component of the vendors’ implementation. I.e. EPIC, Allscripts, Cerner, InterSystems to name a few.

Alternatively, there are products such as Elsevier Medai’s that plug into an EMR to provide analytics. The tools harness data with a set of rules and configuration settings to provide extracts, data mining, reports or exports. There are many other products that can provide this service.

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Figure 12 - EMR Integrated CDS System

4.2.1 Option 2 Strengths and Weaknesses Strengths

• Once in the system no need to login multiple times to access other systems

• Complex algorithms, configurations and rules are applied within the same application

• Support and Maintenance is easier when you have one system

• Maintain consistency of data

• More controlled

• Less expensive, less resources, less maintenance fees

• Clinician confidence in systems that have been trialled and tested

• Supports local, national and international configuration and codes

Weaknesses

• It can be limited when sharing data outside the application

• Development maybe restricted to vendor buy-in

• Sometimes systems do components well, and others components not so well. So, you are not getting best of breed for everything

• Configuration and algorithms can be complex and getting input to meet standardisation can be difficult

• Exporting data to further manipulate data can be restricted in some systems

• Clinical practices move quicker than technology. EMR can often get growth retarded.

Opportunities

• If multiple sites are on an application then sites can work together to influence changes

Threats

• Cost

• Technology is always changing and therefore, another system could be released that is bigger and better and more sophisticated.

• Buy-in from Clinicians

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Conclusion:

EMR applications in the context of clinical decision support are mature and advancing all the time. It is the findings of this paper that the best clinical decision support matrix is covered by using a combination of tools including an EMR and either an inference engine, machine learning tool or BI and although MDM has shown some benefits in banking it has a long way to prove itself in health.

4.2.2 CDS Adoption A recent document from the Institute for Health Technology Transformation which addresses “strategies for managing sophisticated analytic tools in the healthcare industry” aligns with Option 1. The recommendation is for organisations to, “Construct a data warehouse that is the single source of truth for all the data your organisation aggregates.”

Figure 13 - Analytics Framework from Analytics: The Nervous System of IT-Enabled Healthcare

The elements of this diagram are discussed in detail in the source paper, though a few key points are immediately visible:

1. Data governance is key especially in an advance sharing arrangement. This is the first element of the framework and perhaps the most critical for overall success.

2. Picking a project that can be delivered and achieves a measurable benefit (framework line 6) appears to be very good advice, both in terms of achieving a clinical outcome but also in terms of demonstrating the benefit to the executive or board.

3. Measuring patient outcomes also appears valuable, as ultimately CDS is all about ensuring patient safety and optimising the care delivered.

Ken Terry, in the same document referenced above, makes a number of recommendations for those wishing to adopt a CDS system.

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• “Construct a data warehouse that is the single source of truth for all the data your organization aggregates. Ensure consistency of data and terminology in addition to establishing a robust data mapping and cleaning process.

• Track process information such as patient outreach efforts and patient compliance with physician recommendations.

• Change the analytic perspective from episode-based or procedure-based analyses to patient-based and population-wide views. Manage the population for the individual with a longitudinal care approach.

• Ensure data availability is real time and accurate so that the information is timely enough to help clinicians intervene with patients.

• Integrate claims and administrative data with clinical data from EHRs to provide a 360-degree view of patient care.

• Makes sure to engage the right skill sets to enable data schemes and models for actionable data.

• Data governance policies are critical to success – Consider appointing a chief information management officer

• Use predictive modelling and other “big data” decision support tools with the expanded source data. Base this on best practices and establish what to focus on. i.e. diabetes, heart disease, obesity and patient engagement.

• Make sure that all stakeholders within the organization help define the goals of health IT. For analytics to have the desired result, it must meet the financial, care delivery, and operational business needs of the organization.

• Create a culture of using data to treat patients so that the organization consistently collects data and applies analytics to all of the information it needs to manage population health successfully.

• Don’t boil the ocean. Big things have small beginnings. The entire process will need piloting, evaluation and ongoing improvement.”

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4.3 References The following documentation is incorporated and referenced:

1. OCIO web site (CTI related information): http://www.health.vic.gov.au/healthsmart/index.htm

2. NEHTA web site (PCEHR, NASH, Healthcare Identifiers, clinical documents): http://www.nehta.gov.au

3. NEHTA PCEHR and other specifications (registration required): https://vendors.nehta.gov.au/

4. Black Box – Sighted 19.04.2013

http://motorcycleguy.blogspot.com.au/2008/06/clinical-decision-support.html

5. Donald W. Simborg, Don Eugene Detmer and Eta S. Berner,

6. ISO Technical Specification – Health Informatics, Requirements and methodology for Detailed Clinical Models (ISO TS 13972)

7. http://enwikipedia.org/wiki/Clinical_decision_support_system. Sighted 30th April, 2013

8. http://biophys.odmu.edu.ua/bmi/20-CDSS-full.pdf. Biomedical Informatics Computer Applications in Health Care and Biomedicine, Third Edition Edward H. Shortliffe and James J. Cimino (2006 Springer Science+Business Media, LLC) Sighted 30th April, 2013

9. BMC Medical Informatics and Decision Making 2013-05-01 a.e.de.vries, m.h.l.van.der.wal, m.m.w.nieuwenhuis, richard.dejong, rene tiny.jaarsma, h.hillege, r.j.j.m.jorna

10. http://www.fiercehealthit.com/story/harvard-plans-big-data-push-computational-models-how-cells-behave/2013-05-02?utm_medium=nl&utm_source=internal (sighted 7th May 2013) (Address reprint requests to Dr. Timothy J. Ley at Washington University School of Medicine, Division of Oncology, Stem Cell Biology Section, Campus Box 8007, 660 S. Euclid Ave., St. Louis, MO 63110, or at [email protected]. )

11. http://my.gartner.com/portal/server.pt?open=512&objID=256&mode=2&PageID=2350940&resId=2101217&ref=QuickSearch&sthkw=clinical+decision+support+systems. Created 3rd July, 2012

12. http://www.healthcareitnews.com/news/benchmarks-changes-are-afoot-clinical-and-business-intelligence. Reported 22nd April, 2013

13. http://www.heraldsun.com.au/news/national/victorian-man-colin-fiedler-brought-back-from-the-dead-by-australian-first-resuscitation-technique/story-fncynkc6-1226640656309 Reported by Christian Dougherty, May 12, 2013. Sighted 13th May, 2013

14. EMR Benefits and Benefit Realisation Methods of Stage 6 and 7 Hospitals – Hospitals with advanced EMRs report numerous benefits. EMR Benefit Survey 2012, The Advisory Board Company and HIMSS Analytics, Produced by HIMSS Analytics

15. http://www.openclinical.org/dssSuccessFactors.html. Open Knowledge Management for Medical Care. Sighted May, 12th 2013.

16. http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch2.html, sighted 20th May, 2013. KULIKOWSKI Casimir, SHOLM M WEISS

17. Analytics: The Nervous System of IT-Enabled Healthcare, Ken Terry, Copyright Institute for Health Technology Transformation 2013

18. http://www.medicalobserver.com.au/news/diagnostic-errors-claim-4000-lives-annually, sighted 29th May, 2013 author, Byron Kaye

19. http://www.biomedcentral.com/1472-6947/13/54, Medical Informatics and Decision Making Arjen E de Vries, sighted 5th June213

20. http://www.biomedcentral.com/1472-6947/9/17, A national clinical decision support infrastructure to enable widespread and consistent practice of genomic and personalised medicine, sighted 5th June, 2013 Kensaku Kawamoto, published 2009

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4.4 Glossary

Term Description

AMT Australian Medicines Terminology

Analytics In the context of this document analytics and Business Intelligence is the same. It is a system or tool to perform analysis.

AQSHS standards Australian Quality and Safety Health Service standards

Artificial Intelligence Artificial Intelligence systems are intended to support healthcare workers with tasks that rely on the manipulation of data and knowledge.

ARGPM Australian Regulatory Guidelines for Prescriptions Medicines

Australianisation • improve safety and quality of healthcare

• improve efficiency of healthcare

• comply with legal and ethical organisational requirements

• ensure fit for purpose

• meet regulatory and statutory requirements i,e, Australian regulatory guidelines for prescription medicines (ARGPM),

• Pharmaceutical Benefit Scheme PBS

• Australian Medicines Terminology AMT

• Victorian Admitted Episodes Dataset VAED

• Victorian Emergency Minimum Dataset VEMD

• Medical Benefit Scheme MBS

• National eHealth Transition Authority NEHTA

• Health Level 7 HL7

• National Data Dictionary

• International Classification of Diseases ICD10

• Systematised Nomenclature of Medicine Clinical Terms SNOMED CT

• Australian Standards for Secure Messaging Delivery

• the integration to improve the patient journey

And any specific hospital or health services requirements that make core business functions fail if NOT implemented I.e. Oncology module for Cancer Centre, Paediatric Intensive Care Unit, Paediatric Hospital

Bayesian Network The Bayesian network is a knowledge-based graphical representation that shows a set of variables and their probabilistic relationships between diseases and symptoms. They are based on conditional probabilities, the probability of an event given the occurrence of another event, such as the interpretation of diagnostic tests.

Bayes’ Rule Helps us compute the probability of an event with the help of some more readily available information and it consistently processes options as new evidence is presented.

BI Business Intelligence

CDA Clinical Document Architecture

CDS Clinical Decision Support

CDSS Clinical Decision Support System

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Term Description

Computability The ability to solve a problem in an effective manner

CPR Computerised Patient Record (in Gartner report)

DHS Australian Government Department of Human Services, which now incorporates Medicare Australia.

Also Victorian Department of Human Services.

DH Victorian Department of Health

DoHA Commonwealth Department of Health and Ageing

DW Data Warehouse

EBM Evidence Based Medicine (in Gartner report)

eDW Enterprise Data Warehouse

EHR Electronic Health Record

EMPI Enterprise Master Patient Index

EMR Electronic Medical Record

HDA OCIO Health Design Authority

HDF Health Design Forum

HDO Health Delivery Organisation (in Gartner report)

Health Organisation Health Organisations exist for areas related to health, such as and not limited to health services, hospitals, community centres, mental health, health alliances

HIT Health Information technology

HIMSS Health Information and Management Systems Society.

HL7 Health Level 7, a widely accepted standard to support exchange of medical information, both administrative and clinical.

HS Health Service

ICD10 International Classification of Diseases

Inference Engine The act of deriving logical conclusions drawn from what is known or assumed to be true that comes from a knowledge base

Integration To provide the right information at the right place and at the right time and thereby enabling communication between applications. The exchange of electronic messaging. I.e. HL7, CDA

Interoperability Is the ability of diverse systems and organisations to work together (inter-operate) to enhance workflows and processes without barriers and broken data flows.

ISO International Standards Organisation

Knowledge Base The knowledge base holds rules, configuration and mapping. It can also store data depending on the schema.

MBS Medical Benefits Scheme

MDM Master Data Management

NEHTA National eHealth Transition Authority

OCIO Office of the CIO, Victorian Department of Health

PACS Picture Archiving and Communication System

PAS Patient Administration System – a system used for the recording of patient and provider information to support management and coordination of service provision.

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Term Description

PBS Pharmaceutical Benefit Scheme

PCEHR Personally Controlled Electronic Health Record

Real Time Instant, can be seen and acted upon at point of care

ROI Return on Investment

SCTT Service Coordination Tools Templates

SNOMED CT Systematised Nomenclature of Medicine Clinical Terms

VAED Victorian Admitted Episodic Dataset

VCDC Victorian Cost Data Collection

VEMD Victorian Emergency Minimum Dataset

VHSPMF Victorian Health Service Performance Monitoring Framework

VINAH Victorian Integrated Non-Admitted Health, a minimum dataset and reporting specification

vMR Virtual Medical Record

VPHS Victorian Public Health Sector

OCIO Health Design Authoritydocs2.health.vic.gov.au/docs/doc/351D667F062F4936CA257BC8007… · OCIO...

Documents

Transcript of OCIO Health Design Authoritydocs2.health.vic.gov.au/docs/doc/351D667F062F4936CA257BC8007… · OCIO...