Approximately $44 billion in wasteful healthcare spending is attributable to unplanned hospital readmissions.[i],[ii] Interventions that improve care transitions from hospital to home have been shown to curb wasteful spending due to readmissions.[iii] There are many opportunities to improve transitions of care with over 105 million ambulatory patients referred to another provider of care in 2009 and over 34 million patients discharged from the hospital or emergency room or transitioned to a sub-acute setting in 2007.[iv]
Poor transitions stem from poor communication. As patients progress through the healthcare delivery system, there is often poor retention and/or comprehension of the information shared at the time of the visit by the provider. Over half of patients cannot state their diagnoses at the time of discharge and more than a third are unable to explain their medications.[v] 80% of serious medical errors are attributable to hand-off miscommunication between medical providers[vi], and these communication deficits often result in poor outcomes. In an era where internet connectivity is ubiquitous and app proliferation is unprecedented,[vii] a fertile environment exists to augment existing and emerging care transition models using mobile technology.
The immaturity of the care transitions technology marketplace is reflected in a qualitative survey of physicians from the HIMSS mHealth Physician Taskforce who specialize in care transitions and technology. The results suggest several limitations to effectively selecting mHealth for care transitions. These limitations include the following:
The survey of physicians also yielded the following recommendations to evaluate mHealth for care transitions in the future, including:
Recent research from the Institute for Healthcare Improvement (IHI) provides one of several potential resources to help address the challenge of effectively selecting digital health technologies.[viii] Their systematic approach narrows a large number of technologies to a short candidate list that meets an end user’s preferred needs.
This digital health selection framework (DHSF) has been adapted by the Massachusetts Health Policy Commission (HPC) to standardize the vetting of $60 million in software solutions for their community hospitals.[ix] Clinicians and hospital executives can also apply this framework to select new care transitions technology.
Figure: The Digital Health Selection Framework
Key steps in identifying a short list of mHealth solutions using the DHSF is (1) selecting inclusion criteria in order of importance and (2) applying those criteria to a database of technologies. The bulleted list of challenges and recommendations for mHealth selection mentioned above can serve as a starting point for inclusion criteria.
Clinicians and non-clinician providers in hospital and community settings are likely to have different inclusion criteria and priorities. The database used to narrow the list of candidate technologies should include a set of inclusion criteria broad enough to meet the needs of as large of a user base as possible.
Although no databases currently exist that focus exclusively on care transitions, technology. there are databases that include peer-reviewed technologies and focus on care transitions. Examples include the Innovation Exchange from the Agency for Healthcare Research and Quality (AHRQ)[x], the Innovator Database from the Center for Care Innovations (CCI)[xi], and the Innovation Inventory from the Global Lab for Innovation at University of California, Los Angeles.[xii]
With the growing financial incentives to reduce readmissions and the burgeoning of mobile apps, there is an increasing need to apply systematic approaches to more simply and effectively compare and select the best care transition technologies. Identifying these successful selection processes will require more rigorous research and rapid cycle testing of mobile technology and selection frameworks in the future.
[i]The National Learning Consortium (NLC), Stage 2 MU Transitions of Care: Insights from ONC’s IT Fellows’ Pilot Efforts Case Study. 2014 March; (1):1-16.
[i] Jencks SF. Defragmenting care. Ann Intern Med. 2010;153(11):757–8.
[ii] Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009; 360(14):1418–28.
[iii] Verhaegh KJ, MacNeil-Vroomen JL, EslamiS, Geerlings S, de Rooij SE, & Buurman BM. Transitional care interventions prevent hospital readmissions for adults with chronic illnesses. Health Aff (Millwood). 2014 Sep;33(9):1531-9.
[iv] Barnett ML, Song Z, Landon BE. Trends in physician referrals in the United States, 1999-2009. Arch Intern Med. 2012 Jan 23;172(2):163-70.
[v] Berenson R and Horvath J. The Clinical Characteristics of Medicare Beneficiaries and Implications for Medicare Reform. Washington: The Center for Medicare Advocacy, Inc., 2002.
[vi] Solet, DJ et al Lost in translation: challenges-to-physician communication during patient hand-offs. Academic Medicine 2005; 80:1094-9.
[vii] Powell AC, Landman AB, Bates DW. In search of a few good apps. JAMA. 2014; 311(18):1851-1852.
[viii] Ostrovsky A, Deen N, Simon A, Mate K. A Framework for Selecting Digital Health Technology. IHI Innovation Report. Cambridge, MA: Institute for Healthcare Improvement; June 2014.
[ix] Mate K & Ostrovsky A. The “Search for a Few Good Apps” May Be Over. Institute for Healthcare Improvement. 2014. <http://bit.ly/1pXFuQP>
[x] Agency for Healthcare Research and Quality (AHRQ). Innovation Exchange. <https://innovations.ahrq.gov/>
[xi] Center for Care Innovations (CCI). Innovator Database. <http://www.careinnovations.org/knowledge-center/innovator-database/>
[xii] Global Lab for Innovation. Innovation Inventory. University of California, Los Angeles (UCLA). <http://uclainnovates.org/intake-form>