The eHealth readiness of Australia's medical specialists - Final Report

Cluster analysis of attitudes and drivers

Page last updated: 30 May 2011

We identified five eHealth attitudinal clusters of medical specialists based on perceptions of eHealth benefits, adoption drivers and barriers. We further refined these clusters to improve reachability by adding a demographic dimension, the percentage of income derived from the private sector. The most significant influence in defining the boundaries between clusters has proven to be the barriers perceived by medical specialists. We describe the clusters below, in order of their likelihood to adopt, and influence others to adopt, eHealth solutions. The relevant research findings are then summarised in the table that follows.

Cluster 1: eHealth entrepreneurs (24 percent of all respondents)

These specialists have the strongest interest in and use of eHealth solutions, and are willing eHealth participants. Anticipating strong benefits in efficiency, continuity of care and practitioner collaboration, their main concerns are IT compatibility and reliability issues. They are almost twice as likely as other clusters to expect that eHealth will improve patient engagement, relationships and satisfaction.

Internal medicine is overrepresented in this cluster, in part because many of these specialists handle patients with complex care needs over long periods of time, and as a result, have found ways to use eHealth solutions to improve their efficiency. Those in solo or small private practices have often pioneered their own in-house eHealth solutions in an effort to improve workflow. Some practitioners (e.g. rheumatologists) find compelling benefits in computerised decision-making algorithms and documentation for the prescription of highly specialist drugs, while others find electronic records much more efficient for capturing, storing and retrieving patient information.

Though internal medicine practitioners are the most prominent eHealth entrepreneurs, the cluster is well-represented in nearly all specialty groups, so collectively they may engage with most other medical specialists. The cluster’s experience and enthusiasm may well be available to persuade more hesitant practitioners. Engagement strategies should therefore aim to harness this potential energy. They might be engaged in designing and shaping solutions, in defining value propositions for their peers, and in supporting demonstrations of usability and benefits.

Cluster 2: Network adopters (17 percent of all respondents)

These specialists commonly work in an environment, such as public hospitals, where computer use is expected. In these settings, they anticipate eHealth improvements in collaboration and continuity and quality of care, so that internal IT compatibility and reliability are their main concerns. While enthusiastic about the benefits, they are difficult to influence directly because they have restricted influence over their operating environment and so are less likely to control purchasing and adoption decisions within their practices. Accordingly, use of eHealth applications by others in their network is a much stronger driver for adoption than financial incentives or patient demand.

Anaesthesia, emergency medicine, and diagnostics specialists are over-represented in this cluster, largely because these practitioners typically work in hospital environments surrounded by eHealth use. These specialists also tend to be adept at working with technology and interested in the use of computers to improve their workflow.

Network adopters can play an important role as change advocates within their networks of care because they interact with a wide range of other specialists and other healthcare professionals, as well as with patients. As eHealth supporters, they can be used to disseminate information and influence perspectives more broadly within their operating environments.

Cluster 3: Capable but unconvinced (13 percent of all respondents)

These specialists are reasonably tech-savvy, have financial resources and IT support, and are less influenced by potential barriers such as IT compatibility, cost or privacy concerns. They currently use a range of eHealth applications, but only the ones with clear perceived benefits. Relative to the first two clusters, they are much less interested in adopting the eHealth solutions that they do not currently use. For example, 44 percent of the cluster responded that it didn’t need interactive decision support for ordering tests (compared to 18 percent of eHealth entrepreneurs), even though computerised test ordering rates were similar to eHealth entrepreneurs (22 percent vs. 29 percent respectively for ordering pathology tests, and 23 percent vs. 22 percent for ordering imaging).

Practices for which IT failures or delays would be either costly or critical, such as surgeons and emergency specialists, are over represented in this cluster. These practitioners are generally adept at using technology, but are interested only in solutions that improve efficiency without detriment to clinical care, or that improve clinical care without sacrificing practice efficiency.

The capable but unconvinced specialists are unimpressed by the surveyed adoption drivers, with only peer practitioner use holding some sway with them, and even then not greatly. The case for adoption must include clear evidence that the proposed eHealth solution has been implemented successfully by other practitioners whom they respect. Overall, they are not yet convinced that eHealth would help them improve clinical care, though they are open to that evidence. However, they would resist solutions that could cause disruption to the care delivery process in their practice. Evidence that the eHealth solution being proposed to them is an integral part of a broader and positive healthcare reform will be important to them. However, that argument will not be decisive unless they are sure the solution will not cost them practice efficiency.

Cluster 4: Apprehensive followers (30 percent of all respondents)

These specialists see some benefits in eHealth applications, but perceived benefits are heavily outweighed by perceived barriers and risks. For example, they are more than four times more likely to be concerned about malfunction risk than Cluster 3 specialists. They have the most serious privacy concerns, are financially constrained for new investments, and have limited access to IT support. This cluster contains a fairly even mix across all specialties, as it is not closely tied with any specialty-specific characteristics or drivers.

Given the number of perceived barriers and weak perception of benefits, adoption strategies will need to be multi-faceted and address more than a barrier or two major barriers. They will also be waiting for others to act first. In earlier stages, apprehensive followers should be engaged in defining eHealth solutions and delivery models, to ensure that solutions address their needs and concerns. Frequently updated, transparent information on adoption level and momentum, within relevant communities of care, will provide some of the pressure and encouragement they need.

Cluster 5: Uninterested (16 percent of all respondents)

These specialists have the lowest IT usage rates and have negative perceptions of all eHealth benefits. They do not operate in an environment in which computer use is expected, and face many barriers to adoption. They strongly disagree that eHealth applications will bring better patient relationships, engagement or outcomes (in fact, they believe the reverse) and are also less responsive to common influence levers (e.g. financial incentives and peer pressure). The cluster is skewed towards older practitioners who, with retirement looming, have less incentive to adopt new technology.

Psychiatrists and surgeons are over represented in this cluster; two of the least likely segments to integrate computers into their daily work, and who are worried respectively about interference with patient rapport, and malfunctions and downtime (including simply the time taken to find records and download diagnostic images). The more computerised segments, such as emergency and diagnostics specialists, are almost nonexistent in this cluster.

Though active adoption cannot be expected, the cluster cannot be ignored. Some of its members will influence their peers and public opinion, so any strategy must provide them with evidence on clinical care outcomes and practice efficiencies. That evidence will be better regarded if it comes from their peers. Practice managers and support staff are also influential for this cluster, as they often determine computer use within the practice while the specialists maintain a healthy distance from IT. While not interested in eHealth for its own sake, they may be persuaded by the need for them to adopt eHealth solutions as part of an overall strategy to achieve health outcomes. However, there will be a point at which the best approach will be to require, rather than request, adoption.

Although specialist segments often have one or two dominant clusters, most segments are represented across four or five and there are strong eHealth supporters in each specialist segment. This indicates that clinical workflows explain some but not all of the variation in attitudes between specialists (Exhibit 2). How eHealth supporters are leveraged to influence the rest of their segment may differ, particularly where the more resistant clusters (3-5) are in the strong majority.

EXHIBIT 2

(D)

Table 2: Cluster use, perceptions and drivers


(number in brackets reflects percent of segment that use or strongly agree):

Cluster

Current use (examples)

Perceived benefits

Barriers

Adoption drivers

eHealth entrepreneurs

(24% total)

Viewing pathology results (86%)

Viewing/recording notes during consultations (53%)

Electronic health record (59%); paperless (31%)

Continuity of care (72%)

Efficiency (70%)

Collaboration (65%)

External compatibility (30%)

Internal compatibility (26%)

Malfunction and downtime risk (17%)

Financial incentives (56%)

Professional body endorsement (50%)

Network adopters

(17% total)

Viewing pathology results (89%)

Viewing/recording notes during consultations (43%)

Electronic health record (53%); paperless (9%)

Collaboration (77%)

Continuity of care (71%)

Quality of care (65%)

Patient safety (61%)

Internal compatibility (32%)

Malfunction and downtime risk (23%)

External compatibility (19%)

Privacy breaches (13%)

Other practitioner use (16%)

Support staff use (9%)

Professional bodies (9%)

Capable but unconvinced

(13% total)

Viewing pathology results (71%)

Viewing/recording notes during consultations (39%)

Electronic health record (49%); paperless (15%)

Efficiency (41%)

Collaboration (37%)

Internal compatibility (15%)

Malfunction and downtime risk (13%)

External compatibility (10%)

Other practitioner use (12%)

Otherwise negative

Apprehensive followers

(30% total)

Viewing pathology results (46%)

Viewing/recording notes during consultations (31%)

Electronic health record (33%); paperless (12%)

Continuity of care (34%)

Collaboration (31%)

Efficiency (25%)

Malfunctions and downtime risk (58%)

Prefer to wait until technology proven (51%)

Privacy breaches (47%)

Financial incentives (30%)

Professional body endorsement (17%)

Uninterested and IT challenged

(16% total)

Viewing pathology results (20%)

Viewing/recording notes during consultations (18%)

Electronic health record (16%); paperless (6%)

MinimalMalfunctions and downtime (40%)

Prefer to wait until technology proven (36%)

Privacy breaches (31%)

Minimal