Guest Editorial: ROI = QE2 – Improving Healthcare Quality: Effectiveness and Efficiency through IT

• Introduction
• Quality Issues in Health Care
• Health IT as Quality Improvement Tool
• Challenges Ahead
• Conclusion
• References

Introduction
Long before the release of the US Institute of Medicine (IOM) report – "To Err is Human"^{[ 1 ]} – and the intense concerns generated by this report on patient injuries in healthcare system, this author had strongly argued that health information technology (IT) should be used as a proactive strategic weapon to improve quality of care and services delivery in healthcare systems.^{[ 2 ]} However, calls from the health informatics community for health IT to be used as a quality optimiser appear to have been largely ignored over the past 10 years. The IOM report has significantly elevated public concern about and discussions on the quality, or the lack of quality, of healthcare services. Patient safety in the healthcare system has now received unprecedented attention.

Three of the four Health Informatics New Zealand 2005 Conference papers selected for this special edition of Health Care and Informatics Review Online report on the use of information technologies (IT) in clinical care to improve quality of healthcare. Finally, the tide is turning. These papers signal that the New Zealand health industry is beginning to ride the quality improvement wave and recognises the importance of IT in facilitating the delivery of quality healthcare services.

Quality Issues in Health Care
The healthcare industry has gone through three distinct phases of transformation since the 1950s in response to changing population/demographic and economic characteristics:

• 1950s to 1970s: the focus was on increasing service capacity to cope with population growth. Advances in medical and health sciences were shaped by the needs to effectively manage infections and trauma caused by rapid industrialisation.
• 1980s to 1990s: the focus was on reigning in explosive escalation in health care costs spawned by rapid advances in technology. Developments in medical and health sciences were shaped by increasing prevalence in chronic illnesses brought about by multitude of factors such as environmental and life style changes in increasingly affluent societies. The scientific community continues to face challenges to control infections in developing countries and new breeds of infectious micro-organisms such as the human immunodeficiency virus (HIV).
• Post 2000: the IOM report speared a surge in attention to quality in the healthcare system. Serious concerns about lack of healthcare quality have led to legislation in the US that imposes stricter reporting requirements on hospitals and physicians.^{[ 3 ]} The US Joint Commission on Accreditation of Healthcare Organizations (JCAHO) has required hospitals to implement new safe practices.^{[ 4 ]} The US Veteran’s Health Administration has become the leader in implementing system-wide quality strategies, safe practices and training programmes and established quality strategies/programmes research centres.^{[ 5 ]}

There is little doubt that the healthcare industry and governments worldwide have now placed high priority on (or attention to) improving quality in healthcare systems. A Medline literature search on health care quality improvements restricted to papers dated 1999 onward yielded around 2,700 publications. However, progress has been reported to be slow. A report released in 2004 by the US Agency for Healthcare Research and Quality (AHRQ) showed that few improvements in quality have been achieved.^{[ 6 ]} wo important questions emerge from such depressing results:

• With developed countries spending 8-14% of their national GDP incomes on health care, why do healthcare systems worldwide generally perform so poorly?
• Why is it so difficult to achieve quality improvement in the healthcare system while it is a norm in almost all manufacturing industries worldwide?

The root causes behind these problems are attributable to the complexity of the healthcare system, the culture of medicine and a lack of enabling tools to monitor and feed back quality data to healthcare practitioners and other parts of the system.

The healthcare industry is characterised by services provided by professionals who are committed to deliver high quality health care to patients under the guiding principles of "doing no harm" and "advancing health care through research". The majority of these professionals do have lines of accountability but, as professionals, they all practise as highly autonomous individuals. Although quality improvement strategies are intended to bring changes for the good, they have been very difficult to implement as the normal human behaviour of resistance to change is compounded by antagonism toward attempts by those outside the profession to implement improvement changes.

Although the 1999 IOM report heightened public attention to poor quality care, debates on accuracy that close to 98,000 patients are injured by the health care they received continue to rage on.^[7-9] In the absence of population-based, time series data and statistics, adverse events and efficiency studies can only be based on limited sample size and from secondary data such as insurance claims data.^[10] This inherent weakness prompts criticism of adverse events and efficiency studies by sceptics and makes efforts to implement changes in clinical practice much harder.

The healthcare industry probably has the most complex operating environment with tertiary/quaternary referral hospitals hosting 30 to 50 clinical specialties and sub-specialties constantly interacting with each other. Workflow generated by one speciality will have downstream or upstream flow-on effects on other specialties. The more complex a system, the higher the chance for errors to occur. Likewise, any changes to one or more sub-system within a complex system will always have repercussions on the interacting sub-systems. If one clinical specialty or sub-specialty is resistant to change, other interacting specialties are highly likely to be adversely affected and expected quality improvement benefits from a planned change cannot be produced.

Health IT as Quality Improvement Tool
Between February 1996 and January 1998, the IOM convened six roundtable meetings on health care quality. The Roundtable broadly categorised health care quality problems into three groups:^[11]

• Overuse (providing treatment of no value)
• Under use (failure to provide needed/useful treatment)
• Misuse (errors and defects arising from treatment provided).

System inefficiency and ineffectiveness that can result in poor patient satisfaction are other key quality problems that often plague healthcare organisations.

Given these problems and their seriousness, the important question to the health informatics community (and the healthcare industry) is "How does health IT help address these problems?".

System performance and patient satisfaction are areas in which IT can make substantially significant contributions. Health IT can help significantly streamline the operation processes of hospitals. System processes include patient admission, transfer and discharge, diagnostic test orders, scheduling, results reporting, outpatient or follow-up appointments, referrals and discharge summaries, discharge medications, etc. These activities are mainly processed manually by a number of independent departments within the same organisation. Patients often may have to wait from one-half to almost one full day for discharge-related activities to be organised resulting in high level of inconvenience and frustration, not to mention the lost of bed-days for the hospital.

Hospitals with fully integrated or interoperable information systems allow effective information management and sharing. Diagnostic test orders (eg, radiology tests) can be scheduled automatically by the system and coordinated with systems that manage diet ordering and transport scheduling. Discharge planning processes can automatically activate out-patient/follow-up appointment bookings, discharge summary generation and discharge medication ordering from pharmacies. Pre-discharge patient education programmes including diet and medication advice can be triggered and executed at the appropriate times.

Quality improvement programs in the healthcare systems generally belong to the "affect-the-fact" (or product quality assurance) category. This means that quality audits are generally performed after the care has been delivered. While this approach is better than no quality assurance programme, any damages suffered by a patient during the care processes are not always reversible. Clinical information systems (CIS) or electronic healthcare record systems (EHRS) should provide effective tools for real time monitoring of patient’s changing clinical status, providing trending and alert information to clinicians in visually meaningful ways. Such information, when supported by a reliable knowledge base, would aid clinicians in easily detecting early signs of clinical deterioration and prompt early interventions to prevent complications that might follow if conditions are left undetected.

Given the explosive increases in medical and health knowledge, clinicians (especially generalists) are not always able to accurately estimate patient risks without help.^[12] Although disagreements still persist on the question of clinical guideline usefulness and levels of clinician compliance, increasingly the literature appears to indicate that the use of guideline-based decision support systems can produce positive outcomes including clinician compliance and better care quality.^[13-15]CIS or EHRS should be supported by evidence-based decision support systems. While evidence-based decision support may not necessarily be sufficiently effective for preventing the "overuse" problem, it can be highly useful in minimising or preventing "under use" and "misuse". Patient clinical status information from CIS or EHRS when evaluated by evidence-based decision support system can provide clinicians with evidence-based recommendations to facilitate better planning of patient management strategies or real-time revision of care plans. Assisted by well-designed data (clinical status) visualisation tools, such systems can assist clinicians to better detect potential or actual deteriorations in patient status and facilitates early micro-management of the patient by the healthcare team. The end result is that potential problems are detected early, complications are prevented and care quality can be significantly improved.

One of the root causes of resistance to change is the lack of concrete evidence to flag the seriousness of problems and the need for improvements. Clinicians in New Zealand are required to collect a huge amount of data for reporting to Primary Health Organisations (PHO), District Health Boards (DHB) and Ministry of Health (MoH) in addition to routine clinical documentation. They do not receive any feedback on the data they are required to collect and submit (Meeting record, HINZ Seminar on Primary Care Information Strategy, 16 October 2005 – http://www.hinz.org.nz). If analysis results on these data (eg, treatment patterns, treatment effectiveness, resource utilisation, etc) could be returned to the clinicians, not only will the data collection compliance, data completeness and accuracy be improved, clinicians will also be able to see where and why improvements may be required. The strategy of "let the data speak for themselves" is far more effective than coercion.

Challenges Ahead
New Zealand boasts to be one of the nations that has the most highly computerised health care industry.^[16] However, the nation has a long way to go toward maximising the benefits of health IT, especially in achieving quality improvements. There are many challenges that need to be addressed. The first challenge is to establish standards in clinical and measurement data. Under the auspices of the Health Information Standards Organisation (HISO), a number of national projects have been initiated to address information exchange and data standards. The "Logical Observation and Name Code" (LOINC) Phase 1 work has been completed and subsequent phases to cover microbiology, anatomic pathology and radiology terms will begin soon. However, much more remains to be done. Medicinal product terminology, health event summary data set, outcome indicators such as patient safety measures are some of the obvious examples that will generate immediate benefits for quality data capture and utilisation. A number of DHBs are currently planning or implementing regional clinical data repositories. The development of standard data sets for health event summaries (which include discharge summaries and referrals) and for laboratory and medicine data is critically important if the benefits of these regional repositories are to be maximised. Standards for these data are also important for design and implementation of tools to effectively measure quality and harms in healthcare systems. System interoperability has been a major issue since the healthcare industry began its automation ventures. Significant efforts and resources have been invested by the health informatics community internationally to develop interoperability standards such as Health Level 7 (http://www.hl7.org). However, a truly interoperable system in health care still remains elusive. To achieve true interoperability, a multi-tier service-oriented system architecture (SOA) that conforms to international standards must be designed and supported by the health care and health IT industries. Such architecture will feature (figure 1):

• A standard clinical data repository architecture designed based on open standards such as Health Level 7 Reference Information Model
• A data services object tier that provides technology and platform independent data communication services between healthcare applications and the standard clinical data repository
• A clinical service object tier that also provides technology and platform independent clinical information management and decision support services. The clinical service objects interact with the data services objects through Application Programming Interface (API) calls.

Figure 1: Component objects of service-oriented system architecture

The SOA healthcare system design is discussed by this author in detail elsewhere.^{[17 ]}

As regional clinical data repositories become more widespread, privacy and confidentiality issues needs to be adequately addressed. Today, security technologies have matured sufficiently (even in wireless network environments) and continue to solidify to the extent that a high level of trust can be placed on electronic systems. However, privacy and confidentiality policies remain the weakest links. It is not uncommon to see log-on IDs and passwords appearing on "Post-it" stickers attached to clinical workstations in hospitals. It is important that the healthcare industry develops robust security, privacy and confidentiality policies and enforces them properly. Enforcing these policies requires significant investments in appropriate technologies in particular for access control and authentication. The technologies should be extremely easy to use but staff education on the importance of security and privacy procedures is extremely important.

Conclusion
A decade ago little attention was given to quality and patient safety issues. It was generally assumed that the professionals in the healthcare system would do a great job. The IOM report certainly raised the alarm and generated a strong commitment by the industry to implement quality improvement programmes. The US is leading the world in pushing for quality with legislations and "pay for performance" programmes.^[18-20]

However, in the absence of enabling technologies, quality data required to evaluate healthcare quality, effectiveness, and efficiency will be extremely difficult to obtain. It is especially pleasing to see that the healthcare industry has finally begun to embrace the idea of using health IT as strategic weapons for quality improvement rather than as cost cutting tools. Unfortunately, return on investment (ROI) for health IT is extremely difficult to determine. To achieve this critically important goal, quality population-based, time series data are required to assist in cost-effectiveness analysis. Important research works are also required to develop robust and reliable ROI assessment methodologies. This is yet another challenge that the health informatics community needs to take up.

References

1. Kohn KT, Corrigan JM, Donaldson MS. To err is human: building a safer healthcare system. Washington, DC: National Academy Press; 1999.
2. Chu S, Thom J. Information technology as a proactive strategic weapon in healthcare (guest editorial). Journal of Nursing Administration 1994; 24(5): 5-7.
3. Levinson D. New Pennsylvania law requires error reporting for learning purposes. Report on Medical Guideline Outcomes Research 2004; 15 (1): 5-6.
4. JCAHO – Joint Commission on Accreditation of Healthcare Organizations. National Patient Safety Goals for 2006 and 2005. May 2005.  
5. Kizer KW. Re-engineering the Veterans healthcare system. In Ramsaroop, et al (eds). Advancing Federal sector health care: A model for technology transfer. New York, NY: Springer-Verlag; 2001.
6. AHRQ – Agency for Healthcare Research and Quality. National healthcare quality report. Rockville, 2004.
7. Blendon RJ, Des Roches CM, Brodie M, et al. Views of practicing physicians and the public on medical errors. New England Journal of Medicine 2002; 347:1933-1940.
8. Leape LL. Institute of Medicine medical error figures are not exaggerated. JAMA 2000; 284:.95-97.
9. McDonald CJ, Weiner M, Hui SL. Deaths due to medical errors are exaggerated in Institute of Medicine report. JAMA 2000; 284: 93-94.
10. 10. Wennberg JE, Fisher ES, Baker L, et al. Evaluating the efficiency of California providers in caring for patients with chronic illness. Health Affairs, 16 November 2005, pp.526-543.
11. Chassin MR, Galvin RW. The urgent need to improve health care quality: Institute of Medicine national roundtable on health care quality. JAMA 1998; 280(11): 1000-1005.
12. Friedmann P, Brett A, Mayo-Smith M. Differences in generalists and cardiologists perceptions of cardiovascular risk and the outcomes of preventive therapy in cardiovascular disease. Annals of Internal Medicine 1996; 124: 414-421.
13. Bates DW, Teich JM, Lee J, et al. The impacts of computerised physician order entry on medication error prevention. Journal of American Medical Informatics Association 1999; l6: 313-321.
14. Bates DW, Gawande AA. Improving safety with information technology. New England Journal of Medicine 2003; 348: 2526-2534.
15. Bouaud J, Seroussi B, Antoine EC, et al. A before-after study using OncoDoc, a guideline-based decision support-system on breast cancer management: impact upon physician prescribing behaviour. In: Proceedings, Medinfo 10 – the 10th World Congress on Medical Informatics, London, 2001, Part 1, pp.420-424.
16. Didham R, Martin I, Wood R, Harrison K. Information technology systems in general practice medicine in New Zealand. New Zealand Medical Journal 2004; 117:1198.
17. Chu S. From component-based to service oriented software architecture for healthcare, in Proceedings – Healthcom 2005: the 7th International Conference on Enterprise Network and Computing in Healthcare Industry, 23-25 June 2005, Korea, pp.96-100.
18. Endsley S, Kirkegaard M, Baker G, Murcko AC. Getting rewards for your results: pay-for-performance programs, Family Practice Management: March 2004:45-50.
19. Henley E. Pay-for-performance: What can you expect? Journal of Family Practice 2005; l54(7): 609-612.
20. Rosenthal MB, Frank RC, Li, Z, Epstein AM. Early experience with pay-for-performance: from concept to practice. JAMA 2005; 294(14):1788-1793.

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