Aligning Health Information Technologies With Effective Service Delivery Models to Improve Chronic Disease Care

Prev Med. 2014;66:167–172. (doi:10.1016/j.ypmed.2014.06.017)

The collaborative care model of integrating mental health care into primary care settings is well established as an effective model,1 and it has been adopted as a best practice at the national level. Health information technology (electronic health records [EHRs], mobile health applications, and monitoring devices) can be used to support the collaborative care model.

Bauer and colleagues at the University of Washington look at the roles these technologies can play in helping providers and systems manage the influx of newly insured patients in the wake of expanded medical coverage. The authors highlight 5 key principles of collaborative care2 and the ways in which technology can support each area (Figure 1).

Figure 1. Health Information Technologies That Support Collaborative Care

Collaborative Care Is...

  • Mobile devices that support self-care
  • Health literacy materials via DVD/Internet/mobile devices
  • Patient access to records via secure portals; ability to communicate with providers and contribute information
  • Use of evidence-based algorithms in EHRs
  • Use of standardized measures in EHRs
  • Mobile devices for symptom self-report; sensors that gather real-time data
  • Registries for collaborative, safety-net management of patient groups
  • Telemedicine for rural populations
  • Tracking of clinical processes and patient outcomes via registries

First, collaborative care is patient-centered. The authors emphasize that patient engagement is critical to self-management. Patient education materials may be provided in video format, perhaps eliminating literacy limitations. These materials might be offered via DVDs, the internet, or mobile devices. Alerts sent via SMS or mobile apps could help patients with self-management of their illness via reminders to take their medication or attend appointments. Mobile devices can also be leveraged to track symptoms and provide patients (and clinicians) with immediate, specific feedback about development of symptoms. Indeed, a recent study3 cited by Bauer et al shows that patients have high expectations about the potential for mHealth (the use of mobile devices to improve care) to help them become more informed about and manage their illness, as well as communicate with providers.

Second, collaborative care is evidence-based. Evidence-based support for clinical decisions and treatment algorithms can be built into EHRs to help guide decisions about treatment. The authors point out that such support aligns with patients’ increasing demands for evidence-based care, as knowledge about effective treatment becomes ever more widely available to the general public via the internet and social media. Electronic delivery of evidence-based behavioral interventions could also increase the abilities of limited numbers of staff to provide such interventions.

Third, collaborative care is measurement-based. Mobile applications that integrate standardized measurement tools into EHRs are already available. Traditionally, the use of standardized instruments to systematically collect data on symptoms, vital signs, and patient goals has happened only in clinical settings, but these data could be collected in daily life via mHealth applications and mobile devices, potentially increasing the validity of the data. Such data can populate patient registries that will enable providers to rapidly identify which patients are not improving and may need a change in treatment plan.

Fourth, collaborative care is population-based. A patient registry can reduce the likelihood of a patient “falling through the cracks” by enabling members of a care team to identify those in need. Such registries encourage collaboration by allowing multiple care providers, including care managers and consultants, to see the same data on a given patient and take action as needed. Bauer et al also note the role of telemedicine in population-based care; it can increase access to behavioral interventions for geographically difficult-to-reach populations.

Fifth, collaborative care incorporates accountability. The authors point out that the data gathered by patient registries can provide a basis for compensating providers not just on the quantity of services they provide, but also the quality. Such a payment model has been associated with improvement of patient outcomes.4

Bauer and colleagues conclude that the way toward progress involves aligning HIT capabilities with effective clinical models. In a recent commentary, Torous and Baker5 comment upon the technologies discussed by Bauer et al, and they, too, are optimistic about the potential for advancement, pointing out that smartphone-based interventions have already demonstrated success in schizophrenia, bipolar disorder, and alcohol use disorder. Collaborations between academia and industry will be needed to make sense of the data that can now be gathered, since, as they point out, simple collection of data via HIT does not equal an understanding of how those data can be used to greatest effect.5

References

  1. Thota AB, Sipe TA, Byard GJ, et al; Community Preventive Services Task Force. Collaborative care to improve the management of depressive disorders: a community guide systematic review and meta-analysis. Am J Prev Med. 2012;42(5):525–538. doi:10.1016/j.amepre.2012.01.019 PubMed
  2. Patient-centered Integrated Behavioral Health Care Principles & Tasks. University of Washington AIMS Center Web site. http://aims.uw.edu/collaborative-care/principles-collaborative-Care. Accessed February 15, 2016.
  3. Emerging mHealth: Paths for growth. PricewaterhouseCoopers LLP Web site. http://www.pwc.com/en_GX/gx/healthcare/ mhealth/assets/pwc-emerging-mhealth-full.pdf. Accessed February 15, 2016.
  4. Unützer J, Chan Y-F, Hafer E, et al. Quality improvement with pay-for-performance incentives in integrated behavioral health care. Am J Public Health. 2012;102(6):e41–e45. doi:10.2105/AJPH.2011.300555 PubMed
  5. Torous J, Baker JT. Why psychiatry needs data science and data science needs psychiatry: connecting with technology. JAMA Psychiatry. 2016;73(1):3–4. doi:10.1001/jamapsychiatry.2015.2622 PubMed