British Journal of Radiology (2006) 79, 17-23
© 2006 British Institute of Radiology
doi: 10.1259/bjr/23726774
President's conference papers |
Technology solutions for better outcomes: integrated information management in key to productivity increases in medicine
H Requardt, PhD
Group Executive Management, Siemens Medical Solutions, Henkestrasse 127, 91052 Erlangen, Germany
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Abstract
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The challenges to healthcare systems around the world are primarily impacted by two topics: demographic factors and progress in medicine. An ageing population inherently needs more medical services which add financial burdens, in particular, to public healthcare. Since the level of medical education is growing at the same time, we are observing an increased demand for sophisticated (in general expensive) medicine. Drastic changes in financing seem unavoidable. Multiple diagnoses, repeated examinations, trial-and-error, overcapacities and other signs of missing economical considerations are reinforced by reimbursement systems. In a world where, in principle, all information is available everywhere, more than a patient's history should be accessible. Other industries have knowledge management systems in place that make state-of-the-art expertise available everywhere. Intelligent patient databases could consist of learning cycles that (i) enable the individual to benefit from structured knowledge, in addition to personal experience of the physician, and (ii) use the knowledge generated from the individual to extend the database. The novel area of molecular medicine fits perfectly well into these scenarios. Only attached to an IT backbone can the flood of information be managed in a beneficial way. Efficiency improvements in healthcare address the needs of all parties in the system: patients, providers, and payers. The opportunities, however, can only materialize if everyone is prepared to change. IT will set the standards for the biggest challenge in healthcare: The paradigm shift in medicine.
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Introduction
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Demographic developments are placing tremendous pressure on healthcare systems around the world. Although age distribution varies significantly in different countries (e.g. China's one-child policy versus India's fir-tree distribution), problems come down to one common denominator: We are all living longer.
Figure 1
[1, 2] shows the age distribution in more developed regions and the prognoses for 2025. It is obvious that health is a major macroeconomic factor. If we want to avoid the situation that fewer and fewer payers have to support more and more users of healthcare services, we will need to see more elderly people working. The prerequisite for this development is that they stay healthy. Healthcare systems thus would need to prove that the investment in them pays off as a productivity factor.
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Figure 1. The change of age distribution in more developed regions. The qualitative cost curve reflects the current status. If nothing changes, the real overall cost can be the integral over the age distribution multiplied by the cost curve.
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A related challenge is reflected in the fact that a growing population is increasingly demanding to actively participate in medical progress. Mass media and the Internet depict what is possible today; with the majority of research being funded by the public purse. Thus, it is a natural desire that the same paying public also wants to enjoy the benefits that are generated.
The basic question is: How can all of this remain affordable? Cutting cost by cutting services is not helpful for addressing both the need for higher quality care and the necessity to reduce cost. Instead, all contributors to the delivery of healthcare need to ask themselves "How can we do more with less?" If we draw an analogy with industry, this question translates to "What levers do we see to improve efficiency?"
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Innovations drive efficiency
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Medical industries are developing not only more cost effective and reliable systems, but are also generating more and more relevant patient information in less examination time.
Figure 2 shows a standard way of looking at CT datasets. The approximately 2 GB of raw image data that are typically acquired in a 5 s scan are stored in cache memories, are post-processed with volume renderers and can be displayed according to the interpreter's comfort view.
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Figure 2. Volume-rendered abdominal CT image. The underlying image dataset consists of approximately 800 images.
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A different example is given in Figure 3: Not only has the amount of data dramatically increased, but so has the quality. In this case, a high-resolution three-dimensional (3D) image of the moving heart displays the stent structures with superb resolution.
The broadening of the application scope is typical for each of the imaging modalities: Angio suites do excellent 3D imaging with cone beam reconstruction algorithms, linear accelerators deliver kV and MV images, magnetic resonance scanners have left the domain of pure morphologic imaging, and now measure functions in various ways. As an example, Figure 4 shows colour coded diffusion spectral imaging that is highly correlated with the directions of nerve bundles.
The international medical industry has developed many technologies that can be utilized to improve efficiency in diagnostic and therapeutic processes. Figure 5 shows in a schematic diagram how these developments can be locked into the learning cycles of healthcare providers. The potential for cost savings without sacrificing quality of care is clear. It is, however, evident that leveraging this potential is not only a matter of technology; reimbursement systems and workflow structures have to be adjusted accordingly.
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Is more always better?
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The basic question "Do I get enough information about my patient?" is no longer appropriate from a technology perspective. The medical industry has established time-to-market cycles that can rapidly turn a novel clinical parameter into a product standard. Only 6 weeks elapsed between the identification of the SARS virus and the availability of a clinical test. The problem is no longer the lack of data; the problem lies in filtering out the relevant information.
There are various technological solutions for filtering. A widely practiced method uses overlay of images with different measurement parameters. Figure 6 shows an example in which a positron emission tomography (PET) image shows us where to focus in a set of hundreds of CT images. The overlaid images help us to select the slices of interest.
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Figure 6. PET images overlaid to a volume-rendered CT dataset. The primary breast cancer is clearly delineated. Metastasis search is done within the same dataset.
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A totally different approach with similar outcome is represented by "computer-aided detection" (CAD) algorithms. Figure 7 shows CAD-detected polyps within a virtual colonoscopy dataset acquired with CT. These algorithms have now reached a performance level that is comparable with human readers. It is, however, still applicable only for simple structures, but can help us to focus our attention on the more complex features. Progression of CAD into more complex structures will be subject to the availability of standardized reference cases.
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Figure 7. Computer-aided detection (CAD) algorithms detect polyps in a virtual colonoscopy. The sensitivity for polyps  ge; 6 mm is on average 90%; and the median false positive rate is a manageable 3 per volume [3].
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It can be implied that innovation pressure for medical devices will in future not only focus on the generation of more data, but more and more on the generation of "smarter data". Yes, there will be CT scans that do 256 slices. But at the same time there will be an industry focus on systems with two or three X-ray detector systems that can generate not only increased temporal resolution, but also open up new degrees of freedom with respect to contrast by applying different anode voltages in the sub-systems. Figure 8 shows a basic set-up for such a system.
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Figure 8. Multitube CT set-up. The system enables a new degree of freedom allowing for double temporal resolution and/or novel contrast opportunities.
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Overall, the focus of industry will move from "generation of data" towards "exploitation of data". It is evident that information technology is a key enabler for that shift.
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IT enables process optimization
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In a patient-centric system, the ultimate outcome of the treatment is reflected by the status of the patient. The typical patient process in a hospital usually starts with diagnostic steps (radiology, ECG, lab, ...), iterates with various therapeutic procedures (medication, surgery, radiation, ...), and terminates with the recovery of the patient (ICU, ward, rehab, ...). The most competitive healthcare provider will be the one that optimizes the entire procedure chain rather than the individual steps (this does not relieve the individual departments from delivering the best quality; "best" according to cost optimization criteria means "adequate and intelligent"). In industrial analogy this means analysis, mapping and continuous improvement of workflow.
Workflow optimization comprises the moving of patients, resources and information within the healthcare continuum according to certain rules. Everything (including the rules) is subject to best practice shared across all relevant healthcare participants throughout the world.
Workflow can be referenced in "hospital information systems" by so-called workflow engines. An example of what a workflow engine can contribute is given in Figure 9: The emergency treatment of an acute stroke patient is managed by a computer network. The state-of-the-art workflow engine would not only draft a work list, it would also monitor all activities in feedback loops. Cross-checks with rules engines ensure that the patient experiences state-of-the-art stroke treatment procedures.
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Figure 9. Workflow engine editor. The various decision steps reflect the time-critical diagnosis and treatment of an acute stroke. The time window for initiating thrombolysis is computer controlled.
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Figure 9
gives an impression how a workflow engine can be programmed according to the local conditions. It is obvious that workflow engines not only synchronize clinical activities, but also other day-to-day operations, e.g. discharge (paper work needs to be ready, transportation needs to be arranged, room needs to be made up, bed needs to be cleaned, etc.).
Workflow engines will not only change the way care is delivered, but will also define the requirements for newly developed systems. Requirements and job descriptions in both arenas, industry and healthcare services, will be affected.
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The patient is an individual
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The process chain within healthcare environments (prevention 
diagnosis therapy care) is obviously not limited to hospitals. If we look at a schematic development of cancer in Figure 10, we realise that with today's diagnostic methods we detect cancer only at a very late stage with higher cost and lower quality of life. Patient-focused healthcare systems will bring the intervention point forward to an earlier stage of the disease. With early detection and prevention capabilities, healthcare will increasingly be looked at just like every other service industry. The patient will behave like any other customer, but still with one fundamental difference: He/she is not free in selecting the disease.
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Figure 10. Steps for cancer development. Today's procedures detect cancer at a very late stage associated with high treatment cost and reduced prognosis. Early detection schemes lead to cellular and molecular levels; one of the exciting novel areas of "molecular medicine".
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To shift the intervention point in an efficient way, much basic research remains to be done: The complexity of the "omics" (genomics, proteomics, metabolomics) needs to be understood and standardized with respect to the development of individual diseases. The potential, however, is big and every single day new cancer genes are being discovered or proteins identified that originate in specific tissue alterations. The diagnostic industry is asked not only to deliver blood sample tests, but also software modules that make the associated knowledge available.
The individualization, however, is not only subject to the diagnosis of the individual patient. It also needs to give clear recommendations for an optimized treatment. The entire arena of pharmacogenomics will be closely associated with "omics" analysis. Also, specific tumour metabolisms can be clearly understood and thus individually treated. It becomes evident that in scenarios like these, the diagnostic process moves from primary diagnostic to optimized treatment planning and follow up.
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The holistic scenario
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The topics discussed so far lead to a few characteristics of future healthcare systems:- they will be patient-focused and workflow-driven;
- the patient's entire history will be accessible through an electronic patient record (EPR);
- the providers will be in a competitive situation and thus will publish proven outcome statistics to differentiate themselves;
- the capability of sharing best practices with best-in-class providers will be a differentiating factor.
The patient of the future will no longer rely just on the individual experience of his physician, but on the entire medical knowledge that is available. Obviously, the individual experience becomes part of that knowledge, but there are also other contributors. Figure 11 shows a scenario of how the individual patient information can be matched with the available knowledge. The individual treatment plan for the patient is mainly impacted by two elements: (1) the clinical knowledge database with rules for utilization of equipment and drugs, contraindications, standardizations, procedures and others; (2) the EPR consisting of images, lab data, structured reports, "omics", etc.
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Figure 11. Process chart of future treatment planning. Data access for both the patient's individual electronic patient record and a comprehensive knowledge data base are crucial to enable state-of-the-art medical treatment for everyone, everywhere.
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Those databases will be mined by software agents for reference cases with proven outcome data to derive the most promising treatment plans. This enables the primary care physician (PCP) to match his individual experience with all the information that is available in the data stores. The databases will not only be filled with expert knowledge from medicine, but will also include related disciplines like pharmacology, radiation biology, biomechanics and others. In short, the PCP has a real, powerful tool that leaves him with a high degree of confidence that he has done all he can to help the patient.
It will certainly be a long way to reach this scenario, but at the same time it is worth defining and working towards a common vision. Enabling technologies are there to help make this vision reality. Many new problems will come up including topics like data protection, ethics, business models or simply operational realization, and a social consensus will be required to address them all.
Medicine will never become deductive, but managing its complexity will become easier. Although basic work remains to be done, the technological solutions are available today. It is now a question of political desire to launch the paradigm shift in medicine.
Received for publication August 16, 2005.
Accepted for publication September 16, 2005.
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References
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- Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. World Population Prospects: The 2004 Revision Population Database. [Online]. 2005 [cited 2005 March 15]. Available from: URL: http://esa.un.org/unpp/
- Economic Policy Committee (EPC). Budgetary challenges posed by ageing populations: the impact on public spending on pensions, health and long-term care for the elderly and possible indicators of the long-term sustainability of public finances. Brussels. 2001 October 24 (EPC/ECFIN/655/01-EN final). p. 34.
- Bogoni L, Cathier P, Dundar M, Jerebko A, Lakare S, Liang J, et al. Computer-aided detection (CAD) for CT colonography: a tool to address a growing need. Br J Radiol 2005;78:57–62.[Abstract/Free Full Text]
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Abstract
Introduction
