Health Promotion International, Vol. 18, No. 1, 67-77,
March 2003
© Oxford University Press 2003
Using patient-driven computers to provide cost-effective prevention in primary care: a conceptual framework
1Public Health Travelling Fellow, National Health and Medical Research Council of Australia, 2Visiting Scholar, Institute of Health Promotion Research, University of British Columbia, Vancouver, Canada, 3NHMRC Fellow, National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia and 4Department of Health Care and Epidemiology, Faculty of Medicine, University of British Columbia, Vancouver, Canada
Address for correspondence: Anthony P. Shakeshaft, National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW 2052, Australia, E-mail: a.shakeshaft{at}unsw.edu.au
SUMMARY
This paper initially presents a rationale for the cost-effectiveness of using patient-driven computers in primary care services. It specifically defines the concepts of prevention and primary care, prior to outlining the advantages of promoting the implementation of prevention practices in primary care. It argues that greater use of computer technology represents one means of cost-effectively optimizing the integration of prevention into routine primary care, and identifies an apparent disjuncture between the potential of computers and the limited success with which attempts to integrate them into routine primary care services have been met, as evidenced in the published international literature. Among several possible explanations for this disjuncture, such as a possible lack of precision with which computers identify at-risk patients, perceived high costs associated with computers and physicians’ concerns about the inflexibility and the more impersonal nature of computer interactions, is the apparent failure of researchers to utilize well designed and empirically tested models in the planning, implementation and evaluation of computerized care. An outline for such an approach, utilizing the Precede–Proceed model of health promotion planning and the Diffusion of Innovations theory, is presented.
Key words: computers; prevention; primary care
WHAT IS PREVENTION?
Health care prevention refers generically to initiatives that can be broadly positioned along a continuum from primary to tertiary level prevention. Primary prevention initiatives attempt to ensure health problems, or the processes that culminate in health problems, do not occur (Beaglehole et al., 1993
). Examples include educational campaigns and structural and legislative approaches, such as the modification of taxation, advertising and packaging laws, as well as their enforcement (Shakeshaft et al., 1997). Secondary prevention initiatives aim to identify and then terminate or modify existing behaviours or diseases that increase health risks, or the processes that culminate in these, at the earliest possible moment (Beaglehole et al., 1993). Tertiary prevention initiatives aim to stop or slow the progress and sequelae of existing behaviours or diseases that increase health risks, or the processes that culminate in these, even though the basic condition persists (Beaglehole et al., 1993). Secondary and tertiary prevention initiatives regarding drug and alcohol misuse in Australia, for example, have been characterized by a harm minimization approach, as the basis of government drug and alcohol policy and programmes (National Drug Strategy, 1993).
Health care prevention initiatives may be targeted at the general population, at specific subpopulations or at individuals identified as being at increased risk of deleterious health outcomes. Irrespective of the group or individuals at which preventive initiatives are targeted, health behaviour can be defined as any activity undertaken by individuals who believe themselves to be healthy for the purpose of detecting or preventing illness (Green and Kreuter, 1999). The health care system can have a leading role in encouraging individuals to initiate and maintain health behaviours, particularly through primary care services.
WHAT IS PRIMARY CARE?
Principal among numerous attempts to define primary health care is the WHO’s (World Health Organisation) Alma Ata Declaration of 1978 (WHO, 1978), which has subsequently been augmented in various documents [e.g. (Wanke et al., 1995; National Forum on Health, 1997)]. These documents acknowledge the centrality of primary health care in optimizing the health of individuals, and identify the major principles that should characterize primary care. These include high scientific rigour, social acceptability, affordability and accessibility. The last principle emphasizes a notion of equity by which a core set of services, available to all, are complemented by interventions targeted at those who are at increased health-risk. Primary care should also be intersectorial, in the sense that is should recognize that multiple factors determine health, as well as being responsive to the needs and concerns of individuals, and actively involving them in the governance, management and evaluation of services (Mooney, 2000). Ensuring that these principles are manifest in routine practice represents an enormous challenge, to which computers are likely to make an important contribution.
The centrality of primary care is also reflected in its role as the usual first level of contact with the formal health-care system. It is the principal route of access to specialized health services. As such, primary care represents a juncture at which a range of health services can be mobilized and coordinated in order to care for common illness and manage more complex health problems. Importantly, however, primary care is uniquely placed to provide services that promote health and prevent illness.
PREVENTION IN PRIMARY CARE
Arguably, the major advantage of providing prevention initiatives in primary care is that the majority of the general population in many western countries accesses primary health care services. In Australia, for example, ~80% of the adult population consults a primary care physician in a year, with an average of four visits per year (Australian Bureau of Statistics, 1992). These consultation rates are indicative of the potential to parsimoniously deliver preventive health care to a large majority of the general population.
Delivering prevention initiatives to the majority of the general population through primary care is appropriate from the perspective of both the patient and the physician: primary care physicians have high credibility with the public and are, therefore, likely to be influential; they see prevention as an appropriate role for them and the public expects physicians to be involved in prevention initiatives (Cockburn et al., 1987; Slama et al., 1989; Bruce and Burnett, 1991; Price et al., 1991; Bonevski et al., 1996; Makrides et al., 1997). Primary care physicians have also been shown to be effective in modifying a number of patient behaviours associated with increased health risk, including smoking [e.g. (Imperial Cancer Research Fund General Practice Group, 1994)] and excessive alcohol consumption [e.g. (Wallace et al., 1988)]. Furthermore, primary care physicians themselves are amenable to behaviour change. For example, physicians’ behaviour has been modified successfully to achieve an increase in patient screening rates for cervical cancer, blood pressure and cholesterol, as well as improved detection of patient benzodiazepine use (Pierce et al., 1989; Ward et al., 1991; Bonevski et al., 1999).
Improvement of primary care services can also be encouraged at the organizational level. In Australia, for example, high-level political support from both the Australian Medical Association and the Federal Government has been demonstrated by the provision of funding since the 1992/1993 financial year to facilitate development of local primary care networks, entitled Divisions of General Practice. The aims of these divisions include provision of better access to available and appropriate health services, greater continuity of care and improved cost-effectiveness in the delivery of health services (Australian Department of Health and Ageing, 2002). Achieving these aims is also likely to have a beneficial flow-on effect for the health services sector overall. For example, successful prevention strategies in primary care may lessen the burden on relatively expensive specialist care and, at least in Australia, greater use of primary care would require less reallocation of costs between levels of government and fewer changes to the Medicare Benefits Schedule (Andrews, 1995).
CHALLENGES OF PREVENTION IN PRIMARY CARE
Despite these potential benefits, reasons for the perceived under-utilization of prevention initiatives in primary care are unclear (Zappa et al., 1991; Cohen et al., 1992; Griffith and Williams, 1992; Silagy et al., 1992; Gilpin et al., 1993; Stange et al., 1998; Williams and Williams, 1988; Harris and Mercer, 2001), as is the optimum means of integrating prevention initiatives into the routine delivery of primary health care services (Ashenden et al., 1997). A recent review of the effectiveness of different interventions aimed at improving the delivery of preventive services in primary care, which examined 86 comparisons between intervention and control groups, concluded that interventions have been of variable effectiveness and that factors influencing their degree of effectiveness remain nebulous (Hulscher et al., 1999).
However, one systematic review of the literature (Bonevski et al., 1996) identified some common barriers restricting disease prevention delivery in primary care and organized them into levels: structural (lack of education and training, failure of continuing medical education, non-standardized guidelines, low financial incentive); office (time restrictions, inadequate staff support, forgetfulness); patient (patient reluctance, competing priorities); and practitioner (low confidence, delayed or no feedback). Arguably the structural level is the most difficult to address as it essentially requires policy shifts from governments and professional organizations. The Medical Benefits Schedule in Australia, for example, tends to reward episodic, rather than preventive, care, while targeted programmes, such as the Enhanced Primary Care Medicare items and the Practice Incentives Program, only provide financial support for preventive activity among specific groups, such as older people (Harris and Mercer, 2001).
Specific barriers in the other three levels could be addressed in a number of pragmatic ways. With respect to time restrictions and inadequate staff support, for example, primary care nurses are utilized in the UK to deliver a range of primary care services, including immunizations. These additional staff could also be trained to deliver prevention initiatives to complement physician treatment, just as they have previously been taught problem solving skills successfully (Goldberg, 1995). One potential limitation of this approach, however, is that practice nurses may also become too busy to incorporate preventive care into their existing clinical roles.
Another possible solution to addressing barriers at the office, patient and practitioner levels is to make greater use of computer technology. The use of such technology is not only consistent with the WHO’s Alma Ata Declaration of 1978 (WHO, 1978), but is likely to promote the principles of scientific rigour, acceptability, affordability, accessibility, governance, management and evaluation, which this statement espouses.
COMPUTERS IN PRIMARY CARE
Given these potential benefits, it is not surprising that the use of computer technology in clinical settings is not new. Computers have been used previously as reminder systems, data collection and assessment tools (Shea et al., 1996; Shakeshaft et al., 1998a; Bonevski et al., 1999), to provide personalized information to patients on a range of topics, including drugs and alcohol (Shakeshaft et al., 2002), cancer (Jones et al., 1999), weight loss (Bull et al., 2001) and schizophrenia (Jones et al., 2001), and to deliver behaviourally focused, as opposed to pharmacological, interventions in mental health (Selmi et al., 1990; Parkin et al., 1995; Wright and Wright, 1997; Marks, 1999). In addition to these clinical applications, use of such technology for prevention seems to have met with some success: the largest impact of continuing medical education on preventive practices appears to be achieved using computerized feedback and reminder systems, as well as office aids (Davis et al., 1992; Litzelman et al., 1993; Williams et al., 1998). However, attempts to integrate computer technology into routine primary care services clearly require further evaluation (Shea et al., 1996; Wright and Wright, 1997; Bonevski et al., 1999; Marks, 1999; Mitchell and Sullivan, 2001; Thornett, 2001).
There are several possible explanations for the apparent disjuncture between the potential for using computers as prevention and intervention tools in primary care settings and their limited success reported in the literature. First, it could be that computers do not identify at-risk patients with enough precision. This is unlikely, however, as computerized assessment methods have been shown to result in comparable, if not improved reliability and validity, and fewer missing data compared with more traditional assessment methods, such as pen and paper questionnaires and interviews (Sobell et al., 1996; Bonevski et al., 1997; Newell et al., 1997; Shakeshaft et al., 1998b; Shakeshaft et al., 1999). However, there appears to be no evidence, as yet, regarding the test/re-test reliability of computerized questionnaires.
Secondly, it is likely that the cost of installing and maintaining computer technology is perceived as prohibitively high. Although the initial costs incurred in purchasing equipment may be relatively high, this cost is off-set over time. For example, it has been estimated previously that over a 12-month period, one package containing all the necessary equipment for computer-delivered care could cost less than Aus$3 per patient (Shakeshaft et al., 1998b).
Thirdly, it might be that computers are too cumbersome for patients to use effectively. However, recent computer applications show improved speed and efficiency, particularly in comparison to pen and paper assessment methods, resulting from instantaneous tailoring or branching of assessment items, based on patients’ previous responses. In addition, innovations such as touch-screen interfaces, sound cards and interactive software are likely to have improved the user-friendliness of computers for a wide variety of patients (Shakeshaft et al., 1998b).
Fourthly, even if computers are relatively accurate and user-friendly, it may be that patients would simply prefer not to use them. For example, patients might feel inhibited by a perceived lack of computer skills and a reduction in human contact during their treatment. Once patients have actually used computers, however, existing data indicate that computer assessment is acceptable to them in a variety of treatment settings, including primary care (Shea et al., 1996; Bonevski et al., 1997; Newell et al., 1997; Shakeshaft et al., 1998a), and that computer-delivered treatment may also be acceptable to them (Colby et al., 1989; Marks, 1999). Previous studies have identified older patients and female patients as reporting relatively lower levels of computer acceptability (Card et al., 1974; Erdman et al., 1983), while those with previous computer experience or a tertiary qualification reported higher levels of computer acceptability (Card et al., 1974; Kenny and Purvis, 1988; Bonevski et al., 1997). A more recent study using touch-screen technology found no predictors of computer acceptability (Shakeshaft et al., 1998a). That patients may express some concerns prior to using computers suggests the need to maximize user-friendliness and to ensure that instructions for use are both comprehensive and simple to follow.
A fifth possibility is that physicians or other clinical staff have substantial concerns about patient use of computers in clinical settings. Published evidence appears to be supportive of this possibility. Thirty years ago, initial physician enthusiasm for taking patient histories by computer was tempered by their subsequent observations: unnecessary information was accumulated, physician time requirements and workload increased, and data collected did not help make medical decisions (Grossman et al., 1971; Mayne et al., 1972). Integral to this prob-lem may be a lack of clarity regarding the specific type of prevention information that can usefully be collected and fed back to patients and physicians. To illustrate one possibility, the Joint Advisory Group on General Practice and Population Health in Australia have developed an initiative to improve the management of selected behavioural risk factors in primary care: smoking, nutrition, alcohol and physical activity (SNAP) (Joint Advisory Group on General Practice and Population Health, 2001). To operationalize this initiative, patients could answer computerized questions in waiting rooms prior to their consultation and have their risk status for SNAP behaviours fed back to both themselves and their physician, either on-screen or as a print-out.
In the short-term, then, there appears to be scope to improve the means by which computer technology is implemented in clinical practice (Thornett, 2001). At the very least, well designed, theoretically driven and empirically tested models need to be utilized in the planning, implementation and evaluation of computer-based interventions (e.g. Bujnowska-Fedak et al., 2000).
THE PRECEDE–PROCEED MODEL AS A TOOL FOR IMPROVING COMPUTER-BASED INTERVENTIONS
One of a number of such heuristic models that has been used extensively to guide the implementation and evaluation of interventions delivered in primary care is the Precede–Proceed model of health promotion planning (Green and Kreuter, 1999). There are currently >50 published articles relevant to the application of this model in primary care settings (see http://www.lgreen.net/precede/preapps.htm).
The multi-dimensionality of behaviour and, therefore, the need to consider multiple factors in promoting behaviour change, is recognized by the five phases comprising the Precede component of the model: social diagnosis (social costs and quality of life issues); epidemiological diagnosis (burden of illness data such as mortality and morbidity associated with an at-risk health behaviour); behavioural and environmental diagnosis (the contribution of individuals’ behaviour and lifestyle choices, as well as the environment); educational and organizational diagnosis (levels of motivation for change, as well as the extent to which change is possible and would be rewarded); and administration and policy diagnosis (at governmental and institutional levels) (Frankish et al., 1998; Green and Kreuter, 1999). These five stages correspond in reverse order to the evaluation phases (process, impact, outcomes) identified in the Proceed component of the model [Figure 1; (Frankish et al., 1998)].
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The educational and organizational phase of the Precede component identifies three sets of correlates that have been shown to predict the extent to which behavioural change will be sustained over time: predisposing (level of motivation for change); enabling (barriers to, and facilitators of, change need to be identified and overcome or utilized); and reinforcing (the need for behaviour change to be rewarded) factors. These represent a hierarchy that dictates a logical order of intervention: first, a sufficient level of motivation needs to exist; secondly, change must be possible; and thirdly, any positive behaviour change must be rewarded (Green, 1999
). A fourth correlate identified, demographic and socioeconomic status, is not readily amenable to change in primary care (Green, 1999). The identification of predisposing, enabling and reinforcing factors as determinants of behaviour depicts a broad framework within which more formal theories can be located (Green and Kreuter, 1999). Examples of such theories, which have been used previously to help guide the implementation of computer innovations, include the Health Belief Model [e.g. (Szilagyi et al., 1992)], the Theory of Reasoned Action [e.g. (Prin and Mills, 1997)], Stages of Change [e.g. (Aveyard et al., 1999; O’Neill et al., 2000)] and Diffusion of Innovations (Browning et al., 1984; Andersen et al., 1986; Ash, 1997). Using the example of patient-driven computers in primary care, Diffusion of Innovations asserts that the rate of adoption will be influenced by the characteristics of the innovation itself (i.e. the computer), the characteristics of those adopting the innovation (i.e. primary care physicians and patients) and the characteristics of the system into which the innovation is being adopted (i.e. the primary care setting).
More specifically, the rate of adoption depends upon five elements (Rogers, 1995). The first element, knowledge, asserts that awareness of an innovation creates uncertainty in the minds of potential adopters that needs to be reduced to a tolerable level before an innovation will be adopted. The second element, persuasion, highlights that the positive features of the innovation need to be emphasized to encourage its adoption. A problem unique to persuading individuals to adopt preventive behaviours is that so doing may not avoid a future undesirable event, resulting in weak motivation to adopt the innovation. The third element, the decision to adopt, may be expedited by an option to adopt an innovation on a trial basis. Implementation is the fourth element, in which the change agent (usually a researcher in this context) provides technical assistance and tailoring of the innovation to ensure its suitability to a particular setting or to meet some particular need. The fifth element, confirmation, involves feedback and support for those who have adopted the innovation, to ensure their continued satisfaction with it (Rogers, 1995).
These five elements equate approximately with the three components of the educational and organizational phase of the Precede–Proceed model. The knowledge, persuasion and decision elements can be seen to represent predisposing factors, the implementation stage represents the enabling factors, and the confirmation element represents the reinforcing factors. Based on a confluence of the Precede–Proceed and Diffusion of Innovations models, a table of factors relevant to the adoption of patient-driven computer technology as a means of delivering prevention strategies in primary care settings can be constructed (Table 1).
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INTEGRATING PATIENT-DRIVEN COMPUTERS INTO PRIMARY CARE
To ensure the steps in the innovation adoption process outlined in Table 1
are met in practice, an appropriate implementation strategy would comprise at least the four following components.
1. During the planning phase, involve a primary care physician, or some other professional (such as an academic in an appropriate university department), or both, who is well known to the primary care physicians in the geographical area into which the innovation is to be introduced. This is likely to have four main benefits. First, this expert can assist in the initial design of the innovation. In the example of introducing patient-driven computers into the provision of routine care, this person could assist in the development and testing of computer software, have input into how the computers might be accessed most efficiently within each primary care setting, and assist in developing an effective protocol for managing technical problems.
Secondly, the nominated expert could make it easier to identify which of the eligible primary care practices is most likely to comprise physicians who would be classified as innovators (Rogers, 1995). These practices are more likely to agree to adopt the innovation, at least on a trial basis. Identifying which physicians are most likely to be innovators will involve some degree of subjective assessment; however, there are also likely to be more objective indicators. For example, a greater level of participation in continuing medical education programmes, task forces and professional bodies may reflect a greater willingness to consider adopting new innovations at an early stage.
Thirdly, this approach may promote a higher level of homophily between researchers and physicians that is more likely to result in adoption than simply presenting scientific evidence. In particular, academics perceived as experts in the field of behaviour change may have a high degree of credibility with physicians. These three factors relate to the predisposing (Precede–Proceed model) phase of adoption (Table 1).
A fourth benefit of involving physicians is that involved practitioners are ideally placed to provide feedback regarding ongoing tailoring of computers to optimize their relevance in a particular primary care setting. This benefit relates particularly to the enabling (Precede–Proceed model) phase of the adoption process (Table 1).
2. Meet with primary care physicians identified as likely innovators, in order to describe and explain the innovation, including providing information on why it was developed, as well as why and how it works. For example, data showing the drug- and alcohol-related burden of harm in the community could be presented, along with data showing why primary care settings are an appropriate intervention point, including evidence of the acceptability to patients and physicians of providing prevention initiatives in primary care. Using computers to overcome otherwise prohibitive barriers, such as lack of physician time, could also be identified. Provision of this type of information attempts to persuade physicians to adopt patient-driven computer care (Diffusion of Innovation model) and, as such, can also be identified as a predisposing factor (Precede–Proceed model).
To increase further the likelihood of successful implementation, near-peer influence could be invoked by presenting data relevant to the previous successful adoption of patient-driven computer technology in other primary care settings. At this stage, it could also be emphasized that this computer technology could be phased in. For example, a first step could be an assessment of levels of alcohol abuse among physicians’ patients, along with appropriate feedback to both patients and physicians. Step two would then add the intervention phase. Encouraging physicians to identify potential barriers to implementation is also important and could be augmented by findings from previous research identifying likely problems, such as office space and physician time. As identified in Table 1, this process of identifying and addressing potential barriers to the use of patient-driven computers in primary care represents an enabling (Precede–Proceed model) or implementation (Diffusion of Innovations model) strategy.
Finally, appropriate structures should be put in place to facilitate ongoing reinforcement for (Precede–Proceed model), or confirmation of (Diffusion of Innovations model), the decision to adopt patient-driven computers. Specifically, agreement should be reached on: appropriate mechanisms for feedback both to and from physicians and patients; the feasibility of re-inventing the innovation to improve its suitability to a particular primary care setting; and a protocol for the provision of technical assistance.
3. Once these issues have been resolved, the initial implementation of patient-driven computer technology into one primary care practice is likely to be an influential reinforcing factor in determining whether the innovation will be taken up by other primary care practices. Given the particular importance of ‘near-peer’ communication (Rogers, 1995), it is likely that early successes will enhance the subsequent use and sustainability of the innovation.
4. In addition to identifying strategies to encourage primary care physicians to adopt patient-driven computer technology into routine clinical care, the Precede–Proceed and Diffusion of Innovations theories have implications for provision of patient interventions. For example, interventions for patients not motivated to achieve behavioural change could focus primarily on increasing levels of motivation (predisposing factors), either instead of, or as well as, encouraging overt behaviour change. Alternatively, interventions for those with sufficient motivation could focus on encouraging overt behaviour change, in particular identifying the barriers and facilitators of behaviour change that would need to be overcome or could be utilized (enabling factors). In either case, interventions should also incorporate appropriate encouragement to patients (reinforcing factors) to promote sustained behaviour change.
FUTURE RESEARCH
Arguably the most pressing need is for evidence from scientifically rigorous research, such as randomized trials, pertaining to the cost-effectiveness of patient-driven computers in primary care settings: there is currently very little evidence regarding the effect of primary care computing on patient outcomes (Mitchell and Sullivan, 2001). Possibilities for resolving problems related to the enabling phase of the innovation adoption cycle could also be examined. For example, web-based technology could provide a means for self-assessment (predisposing), ongoing encouragement to sustain behavioural changes (reinforcement) and access for specific subgroups under-represented in primary care, such as young males. Other applications of computer technology could also be explored: computers may be a useful tool for triage; facilitating the monitoring of trends over time; assessment of practice performance indicators; and collection of data relevant to primary care outcome measures. Exploring the extent to which flexibility can be built into patient-driven computer programmes is also important: those more able to be tailored to individuals are more likely to be effective (Bull et al., 2001).
Finally, even if computers do prove to be effective, there is still much uncertainty surrounding which specific mechanisms are most responsible for achieving behaviour change. Further work could help isolate the effect of the computer (is it using the computer that is critical, or is the effectiveness of the computer tied to something inherent in an intervention?) and the effect of the primary care setting (would different results be obtained in different settings?). The potential of computer-based treatment for geographically large countries with relatively isolated rural and remote communities, such as Canada and Australia, is particularly exciting and is likely to be enhanced by the development of web-based intervention programmes (Fotheringham et al., 2000).
For the present, the Precede–Proceed and the Diffusion of Innovations models offer frameworks to help understand better the factors that may influence computer use in primary care. This is not to claim that these models are necessarily the most appropriate; however, they both offer a reasonably clear and well known set of theoretical principles that might usefully guide implementation strategies. For example, identification of predisposing factors (knowledge, attitudes, beliefs and values) may help clarify the motivation of both patients and providers for using computer-assisted strategies. To the extent that patients and providers are motivated to utilize computer technology more effectively, better understanding of enabling strategies may serve to highlight factors that influence the actual implementation of a given computer-assisted initiative, such as time, skills and accessibility. Exploration of reinforcing factors will help identify the intrinsic and extrinsic rewards for utilizing computers in primary care. Without such rewards, it is likely that adoption and success of patient-driven computer technology will be limited.
ACKNOWLEDGEMENTS
A.P.S. was supported by a grant from the National Health and Medical Research Council of Australia. The authors acknowledge the valuable assistance provided by administrative and research staff at the Institute of Health Promotion Research, University of British Columbia, Vancouver, Canada, in the preparation of this paper.
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