Abstract

Summary: Urban, underserved populations experience significant disparities in the screening, prevention, and treatment of chronic disease. Electronic risk screening provides one method of eliminating disparities in the identification of risks, while limiting the burden on providers. This paper reviews the process, success, and challenges of implementing a tablet-based, electronic risk screening pilot study in two Connecticut urban safety-net primary care centers. This project successfully screened 146 patients for 12 risk factors and medical conditions and demonstrated a significant increase in the identified detection rates of these diseases compared to a control group of 129, with high patient acceptance. There were several challenges to the implementation of the screening intervention, including integration of technological environments, limited clinical resources, and challenges in implementing a new process into clinic workflow. Study-based recommendations are made for the successful future implementation of electronic screening, including: developing a critical care pathway playbook, inclusion of all levels of staff on workflow process development, identification of a project champion, and integration of technological system issues.
Keywords: mHealth, screening, implementation, primary care, patient reported outcomes, risk, assessment, urban.
Citation: Staeheli M, Aseltine RH Jr, Warren N, Seagriff N, Gould B. Electronic screening to increase identification of risk: lessons learned in the implementation of an mHealth risk assessment strategy in an urban primary care environment. J Participat Med. 2017 Feb 8; 9:e3.
Published: February 8, 2017.
Competing Interests: The authors have declared that no competing interests exist.
Funding Source: The project described in this paper was funded by the Connecticut Health Foundation.

Introduction

With half of all Americans living with a chronic disease, and a quarter with more than one,[1] preventing and treating these conditions are one of the greatest and most costly challenges facing primary care. Chronic conditions represent 7 out of the top 10 causes of death and 86% of all healthcare expenditures in the United States. [2][3] Patients in urban safety net settings, who are more likely to be poor and non-white, [4][5][6] are at even greater risk for chronic diseases, and the treatment of these conditions accounts for over three quarters of Medicare spending. [7] It is well document that these patients are also more likely to experience disparities in access to care and to high quality care for chronic diseases like cancer, diabetes, oral, and mental health conditions. [4]
[5][8][9][10][11][12][13] For many medically underserved patients, primary care may be their only opportunity to access screening, prevention, and treatment for their chronic conditions.

Preventive care, including screening, represents a key strategy in addressing the disparity of chronic conditions for urban, underserved populations, although these patients also remain far behind in their access to and use of these services. [4][14][15][16] As the first step in prevention, screening for chronic conditions is associated with an increase in patients’ health-promoting behaviors. [17][18]

Comprehensive screening remains a challenge, however, despite its benefits. [19] The US Preventive Services Task Force (USPSTF) recommends screening protocols for 53 medical conditions[6] the completion of which, along with other preventive services, would require more than 7 additional hours a day per provider in primary care to fully address. [20] In urban safety net primary care, considering scarce personnel and structural resources, delivery of preventive service creates an even greater burden, [14][15][21] and screening often drops down on the list of tasks that must be accomplished during a short visit.

While some screening (eg, cancer, diabetes, or asthma), requires diagnostic tests or imaging, others require information directly from the patient (eg depression, smoking, or alcohol use). [22][23][24] Patient involvement in providing risk information for screening is one method of achieving a system that is responsive to patient experiences and needs, and accounting for time and personnel scarcity. Health information technology can aid clinical decision making by capitalizing on the time the patient spends in the waiting room, allowing providers to prioritize care in the clinical encounter. [25][26][27]

This paper presents the case study of an innovative mHealth approach to comprehensive electronic health risk screening and reviews challenges, opportunities and lessons learned.

Case Presentation

A quasi-experimental design was used to assess the rates of obesity, smoking, fall risk, osteoporosis risk, alcohol abuse, depression, post-traumatic stress disorder (PTSD), domestic violence, basic needs, dental care needs, and need for colonoscopy among, adult patients at an urban primary care clinic in Connecticut.

This clinic is a small, urban community health center site with two providers, two medical assistants (MAs), a nurse and two front desk staff. Primary care services are offered, with onsite behavioral health and dental care employing a “warm-handoff” method of referral in which the medical provider introduced the patient to the dentist or therapist to either make an appointment or be seen for immediate care (determined by patient preference.) Prior to implementation of the project, all clinic staff members were trained on the need for comprehensive risk screening and preventive care, use of the tablet technology, the risk assessment tools and their interpretation, and collaborated in development of workflow processes.

All participants were 18 years of age or older and were current primary care patients at the clinic, seen between August and December, 2013. For the intervention group, all patients who had not previously completed the screening questionnaire and presented to the clinic for a physical exam, an urgent care visit, walk-in visit, routine follow up, or well visit were eligible for screening. Patients did not participate in the study if they spoke a language other than English or Spanish, had a disability that prevented completion of the screening, or were noted to be in acute distress. Patients who did not complete the screening were typically seen during periods when the clinic was very busy or when receptionist coverage was limited. Results for patients in the intervention group were compared to a control group of primary care patients who were seen at the same clinic in two weeks of August, 2013 prior to the implementation of the risk screening intervention.

Demographic characteristics of clinic patients participating in the intervention and control groups are presented in Table 1, which indicates that the majority of patients are non-white, in middle age range, female, and using public insurances.

Table 1. Demographic characteristics of participants, by percentages.

For the intervention condition, clinic patients were asked by receptionists to complete the electronic risk screening questionnaire in the waiting room after checking in for their appointment. This electronic platform, designed by OpenClinica and Dimagi, Inc, allowed self-reporting by patients using a touch-screen tablet incorporating dynamic branch logic to capture responses triggered where needed by prior responses (eg, to navigate between two tiers of a screening instrument, such as those predicated on age or a particular behavior). The tablet tool supported English and Spanish versions of the instrument, as well as audio versions for patients with low literacy or vision impairment. Screening responses were transmitted through a secure wireless network to a secure server where they were automatically scored. Results and recommendations for referral or further followup were summarized on the tablet and made available to the clinician.

For the intervention group, the primary analytic outcomes consisted of changes in measured prevalence rates as demonstrated through screening results obtained during the target appointment. Where possible, standardized and validated measures in both Spanish and English were used to assess needs and risk of medical condition (listed in Table 2).

Table 2. Health domains and risk screening guidelines and tools.

For health domains in which validated measures were not available (eg, dental health and basic needs), focus groups of professionals and experts were consulted to generate three questions each that would be indicative of the need for care. For the control group, outcomes were determined through review of EHR on the target appointment. For example, the presence or absence of the medical issue in the target appointment was determined based on the problem list and clinical notes sections of the patient’s paper medical record or EHR. If a patient had been identified as depressed during the target appointment, this was indicated as a positive indication of depression in the chart review.

Table 3 presents the contrast between the proposed and actual screening processes. Over the course of 32 screening days of the intervention, 146 patients (46.5%) out of 314 eligible patients (according to criteria outlined above) completed the screening, and were compared to a control group of 129 patients seen in the clinic prior to the intervention. Patients reported that the tablets were easy to use and that they felt the information would be useful in their health care. A quarter of the patients struggled with the technological aspects of the tablet and required assistance, while only three patients refused to participate.

Table 3. Proposed vs. implemented screening process.

Discussion

Implementation of the screening protocol can be broken down into two process segments: screening patients and then addressing screening results so that patients benefit from them. This pilot project demonstrated success in the first segment of the process, although the data collection process was severely hampered by lack of wireless connectivity and data interoperability in the systems, which are discussed below. These challenges meant that some patients who were eligible to complete the screen were not able to, that there were periods of days or weeks when tablets were not used, times when staff were frustrated or over-burdened, all of which impeded the collection of valuable data and made it difficult to determine whether the goals of the project were met. While an in depth review of screening results is beyond the scope of this paper, comparison of the intervention to the control group demonstrated that the electronic risk-screening tool identified a substantially higher number of health risks (see Table 4). Positive screening results led to follow up care for more individuals in the intervention group than in the control group, illuminated new service needs, and contributed to a more complete health profile of the clinics’ underserved, urban populations.

Table 4. Frequencies of risk conditions, by percentages.

Challenges

The Technological Environment

The second segment of the screening process, management of results and follow up care, proved more challenging. Technology barriers were: 1) technology environment and culture, and 2) integration of systems, including EHR integration. This clinic had a sophisticated technological environment with IT support and an EHR, and staff members felt comfortable using the tablet technology and helping patients use it. Because they were so used to sophisticated technology, they expected a high level of functionality and a fast-paced technological workflow. They were frustrated when there were the inevitable “glitches” or interruptions involved in introducing a new technology.

The second barrier to smooth implementation of this technology was the clash between the clinical IT infrastructure (eg, wireless connectivity, EHRs, scheduling software) and the portable tablets and screening software. While data were generated on the portable device, these data were stored on clinic servers, and Wi-Fi was required by the tablets to function. However, at the time of implementation, the clinic had limited wireless networks of insufficient reliability to support the tablet technology. The improvement of these networks was a low priority for IT because clinic functioning was based on a wired network and wireless malfunction did not impede other clinical processes. Additionally, discrepancies in the interface of clinical and tablet software created repeated malfunctions in the communications between tablet devices and server/databases, leading to many days of tablets being unusable.

Challenges in the interface of technologies also affected the clinical workflow. However, the technology barrier was caused by the difficulty and expense of integrating data generated by the tablets seamlessly into the EHR. In essence, the process of integrating the tablet screening data into the EHR could not be automated. As a result, this presented an issue in workflow for the staff, when the additional step of an MA entering screening results into the EHR became necessary, reducing efficiency and increasing staff frustration.

The “Now What” Problem

Clinical staff expressed concern about three issues once patients had completed the screening:

1. Talking to patients about difficult subjects. At the introduction of the risk-screening process, many providers expressed some apprehension about talking with clients about difficult topics like a patient’s experience of domestic violence, alcohol abuse, depression symptoms or financial difficulties. They reported that they sometimes felt ill prepared to discuss topics that were once beyond the purview of a primary care visit (for example, food insecurity), but that they felt were crucial to patient health.
2. Providing referrals based on patients’ newly discovered risks. Lack of referral resources for patients with limited financial or insurance options also proved to be a consistent source of frustration for providers. The clinic had integrated behavioral health and some dental resources, which alleviated a major referral burden. However, there were few obvious resources to address problems like food insecurity or specialist referral. When staff was able to identify some community resources for patients, providers reported feeling better able to address the risks identified by the screening tool. Staff expressed frustration at often being unaware if patients had seen community providers and being unable to access information about the disposition of those referrals if the provider had not contacted them directly.
3. Insufficient time during clinical visit. Providers universally articulated the challenges of addressing multiple serious and perhaps chronic medical and psychosocial issues illuminated by the screening within a 15-minute appointment in which they must also address the presenting issue, like an ear infection or diabetes management.

Challenges in Staffing and Workflow

Workflow challenges constituted the third barrier to implementation related to: 1) who would do what and when, 2) staff turnover and learning curve, and 3) limited time. Research project staff worked with medical staff to identify a workflow for the implementation of screening process based on the differences in environment and staffing. Despite that, staff were concerned about which groups of personnel would be responsible for which segments of the screening process and how patients and tablets would be “handed off” from one group to another, particularly if some segments of staff felt workflow in this project (and other areas of clinic work) to be inequitable. Their concerns about clinical resources, and the effect of limited resources, directly affected the implementation of the screening tool. Clinical providers were routinely double or triple booked for patient appointments and reported needing additional nursing support. Providers reported that their patients were high need, with complex sets of problems, and that they did not have enough time to attend to these needs adequately. Clinic staff reported that they felt the introduction of a tablet to conduct some of the required screening would benefit their patients, but that they were concerned with work overload and the complexity of the process. The clinic also reported needing additional front desk resources, as existing staff felt overburdened by registering patients for appointments, taking phone calls, and attending to provider and patient requests, while also managing the tablet distribution and collection.

Conclusion

This project also demonstrated that electronic screening based on patient reported outcomes offers new information about patient risk for chronic diseases, information that is otherwise challenging to capture efficiently and effectively. This project proposes one method for providing comprehensive screening to meet USPSTF guidelines in a non-biased and evidence-based way, with fewer screening burdens to providers and more patient involvement. Doing so will provide traditionally marginalized patients with more opportunity to receive preventive care or treatment for complex diseases, thus potentially addressing health disparities influenced by provider/staff attitudes or biases and differences in resource distribution.

While obtaining the screening data was the smoothest component of the project, getting that screening data into the exam room for use in the patient encounter proved to be challenging. Several anticipated barriers to the implementation of this project (stolen tablets or patients not knowing how/not wanting to use tablets) turned out to be unfounded. Other barriers that we had either inadequately prepared for or had not expected became difficult obstacles. Several recommendations have emerged as a result:

• Critical care pathways, complete with a list of resources and potential providers to whom to refer, need to be developed and made available to all providers at the outset of the implementation;
• The technology used needs to be very stable and seamlessly integrated into existing clinical IT structures, so that tablets work reliably, wireless capacity is allocated for communication between tablets and screening database/repository, and screening results are presented to providers easily in a seamless merge with EHR. Apparatus of data collection may include hardwired kiosk system, or accessibility from smart phones, computers, or patient portals;
• “Champions,” staff members who are enthusiastic about the contributions of screening to the clinical enterprise, willing to be point persons for staff concerns and questions, and empowered to demand adherence to screening protocols when clinic staff are “stressed” or overburdened, must be identified;
• Nursing support staff or MAs may be the people best qualified to address the challenges posed by this type of intervention because of their primary role in preventive services, care coordination and patient contact. These staff members could be trained to review risks and determine next steps based on clinical guidelines and make referrals via standing orders, of which the primary care provider could be informed, and could countersign and intervene if necessary; and
• Workflow processes should be established with representatives at all levels of clinic staff to adequately assess and address staffing shortfalls or “stress points”, especially so that these processes can be flexible in response to changing care environments.

Though this project was challenged by the rapid technological innovation and adoption that made some of the tablet technology and clinical IT systems incompatible, there is also a philosophical barrier between the mHealth and clinic technological systems. Health care IT has historically been oriented to protecting data, rather than facilitating access to data from external sources, for fear of vulnerability. Introduction of a new technological process in which portable mHealth devices (like tablets) must work with healthcare IT infrastructure or send to or receive data from IT infrastructure represent a growing challenge, particularly in clinical settings with disparities in access to technological innovation and support. More and more, both patients and providers are expecting mHealth technology to support clinical operations and improve the patient-centeredness of health care. This current intervention demonstrates that these interventions are acceptable to patients and providers, as long as barriers and challenges are addressed, the experience is a seamless as possible, and the information gained is useful. As technology continues to be integrated into clinical operations, and as primary care patients present with more complex chronic diseases, [1][49] screening methods that reduce staff time and effort, while leveraging the experience of patients, provide an opportunity for safety net primary care clinics to address the charge of preventive services for populations that have been traditionally underserved.

References

1. Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: A 2012 update. Prev Chronic Dis. 2014 Apr 17;11:E62. ↩
2. Deaths and Mortality. Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/nchs/fastats/deaths.htm. Accessed October 7, 2015. ↩
3. Gerteis J, Izrael D, Deitz D, LeRoy L, Ricciardi R, Miller T, Basu J. Multiple Chronic Conditions Chartbook. AHRQ Publications No. Q14-0038. Rockville, MD: Agency for Healthcare Research and Quality; April 2014. ↩
4. Primary Care Access: An Essential Building Block of Health Reform. National Association of Community Health Centers. Available at: http://nachc.org/wp-content/uploads/2015/06/PrimaryCareAccessRPT.pdf. Accessed February 4, 2017. ↩
5. 2008 National Healthcare Disparities Report. Rockville, Maryland: Agency for Healthcare Research and Quality; 2009. ↩
6. Agency for Healthcare Research and Quality. U.S. Preventive Services Task Force published recommendations. Available at: http://www.uspreventiveservicestaskforce.org/BrowseRec/Index/browse-recommendations. Accessed February 2, 2015. ↩
7. Centers for Medicare and Medicaid Services. Chronic conditions among medicare beneficiaries, chartbook, 2012 edition. 2012. ↩
8. Office of the Surgeon General. Mental health: Race, culture, and ethnicity. . 2001. ↩
9. Alegría M, Chatterji P, Wells K, et al. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv. 2008;59(11):1264-1272. ↩
10. Anderson LM, Scrimshaw SC, Fullilove MT, Fielding JE, Normand J. Culturally competent healthcare systems: A systematic review. Am J Prev Med. 2003;24(3 SUPPL.):68-79. ↩
11. Blanco C, Patel SR, Liu L, et al. National trends in ethnic disparities in mental health care. Med Care. 2007;45(11):1012-1019. ↩
12. Chinman MJ, Rosenheck R, Lam JA, Davidson L. Comparing consumer and nonconsumer provided case management services for homeless persons with serious mental illness. Journal of Nervous & Mental Disease. 2000;188(7):446-453. ↩
13. Chartier K, Caetano R. Ethnicity and health disparities in alcohol research. Alcohol Res Health. 2010;33(1-2):152-160. ↩
14. Partnership for Prevention. Preventive care: A national profile on use, disparities, and health benefits. 2007. ↩
15. Fiscella K, Holt K. Impact of primary care patient visits on racial and ethnic disparities in preventive care in the United States. Journal of the American Board of Family Medicine. 2007;20(6):587-597. ↩
16. Fiscella K, Holt K, Meldrum S, Franks P. Disparities in preventive procedures: Comparisons of self-report and Medicare claims data. BMC Health Services Research. 2006;6. ↩
17. Bankhead CR, Brett J, Bukach C, et al. The impact of screening on future health-promoting behaviours and health beliefs: A systematic review. Health Technology Assessment. 2003;7(42). ↩
18. Rasmussen S, Thomson J, Kilsmar J, et al. Preventive health screenings and health consultations in primary care increase life expectancy without increasing costs. Scadinavian Journal of Public Health. 2007;35:365-72. ↩
19. Southern WN, Drainoni M-, Smith BD, et al. Physician nonadherence with a hepatitis C screening program. Qual Manag Health Care. 2014;23(1):1-9. ↩
20. Pollak KI, Krause KM, Yarnall KSH, Gradison M, Michener JL, Østbye T. Estimated time spent on preventive services by primary care physicians. BMC Health Serv Res. 2008;8. ↩
21. Fiscella K, Epstein RM. So much to do, so little time: Care for the socially disadvantaged and the 15-minute visit. Arch Intern Med. 2008;168(17):1843-1852. ↩
22. McKenna S. Measuring patient-reported outcomes: Moving beyond misplaced common sense to hard science. BMC Medicine. 2011;9(86). ↩
23. Deshpande PR, Rajan S, Sudeepthi BL, Abdul Nazir CP. Patient-reported outcomes: A new era in clinical research. Perspectives in Clinical Research. 2011;2(4):137-144. ↩
24. Higgins J, Green S. What are patient reported outcomes? In: Cochrane handbook for systematic reviews of interventions. 5.1.0 ed.; 2011. ↩
25. Anand V, Carroll AE, Downs SM. Automated primary care screening in pediatric waiting rooms. Pediatrics. 2012;129(5):e1275-e1281. ↩
26. Fothergill KE, Gadomski A, Solomon BS, et al. Assessing the impact of a web-based comprehensive somatic and mental health screening tool in pediatric primary care. Academic Pediatrics. 2013;13(4):340-347. ↩
27. Smith AJ, Skow A, Bodurtha J, Kinra S. Health information technology in screening and treatment of child obesity: A systematic review. Pediatrics. 2013;131(3):e894-e902. ↩
28. Bohn, M. J., Babor, T. F., & Kranzler, H. R. The alcohol use disorders identification test (AUDIT): Validation of a screening instrument for use in medical settings. Journal of Studies on Alcohol. 1995;56(4):423-432. ↩
29. Reinert DF, Allen JP. The alcohol use disorders identification test: An update of research findings. Alcohol Clin Exp Res. 2007;31(2):185-199. ↩
30. Garg A, Butz A, Dworkin P, Lewis R, Serwint J. Screening for basic social needs at a medical home for low-income children. Clinical Pediatrics. 2009;48(1):32-36. ↩
31. Fiscella K, Franks P, Gold M, Clancy C. Inequality in quality: Addressing socioeconomic, racial and ethnic disparities in health care. JAMA. 2000;283(19):2579-2584. ↩
32. Davila RE, Rajan E, Baron TH. ASGE guideline: Colorectal cancer screening and surveillance. Gastrointest Endosc. 2006;63(4):546-557. ↩
33. Bhat SK, East JE. Colorectal cancer: Prevention and early diagnosis. Medicine. 2015;43(6):295-298. ↩
34. Pox C, Schmiegel S, Schulmann K, Stemmler S, Schrader H, Adamietz IA. Prevention and diagnosis of colorectal cancer. Onkologe. 2009;15(10):1033-1048. ↩
35. Murff HJ, Peterson NB, Greevy RA, Shrubsole MJ, Zheng W. Early initiation of colorectal cancer screening in individuals with affected first-degree relatives. Journal of General Internal Medicine. 2007;22(1):121-126. ↩
36. Emmons KM, Lobb R, Puleo E, Bennett G, Stoffel E, Syngal S. Colorectal cancer screening: Prevalence among low-income groups with health insurance. Health Aff. 2009;28(1):169-177. ↩
37. Kroenke K, Spitzer RL. The PHQ-9: A new depression diagnostic and severity measure. Psychiatr Ann. 2002;32(9):509-515. ↩
38. Merz EL, Malcarne VL, Roesch SC, Riley N, Sadler GR. A multigroup confirmatory factor analysis of the patient health questionnaire-9 among English- and Spanish-speaking Latinas. Cult Diversity Ethnic Minor Psychol. 2011;17(3):309-316. ↩
39. Sherin KM, Sinacore JM, Li X-, Zitter RE, Shakil A. Hits: A short domestic violence screening tool for use in a family practice setting. Fam Med. 1998;30(7):508-512. ↩
40. Shumway-Cook A, Ciol AM, Hoffman J, Dudgeon JB, Yorkston K, Chan L. Falls in the Medicare population: Incidence, associated factors, and on health care. Phys Ther. 2009;89(4):324-332. ↩
41. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the timed up and go test. Phys Ther. 2000;80(9):896-903. ↩
42. Okumiya K, Matsubayashi K, Nakamura T, et al. The timed “up & go” test is a useful predictor of falls in community-dwelling older people. J Am Geriatr Soc. 1998;46(7):928-929. ↩
43. World Health Organization. BMI classification: Global database on body mass index. http://www.who.int/bmi/index.jsp?introPage=intro_3.html2012. ↩
44. NIH. The practical guide: Identification, evaluation, and treatment of overweight and obesity in adults.2000;00-4084. ↩
45. McLeod KM, Johnson CS. Identifying women with low bone mass: A systematic review of screening tools. Geriatr Nurs. 2009;30(3):164-173. ↩
46. Gaynes. B., DeVeaugh-Geiss, J., Weir, S., Gu, H., MacPherson, C., Schulberg, H., Culpepper, L., & Rubinow, R. Feasibility and diagnostic validity of the M-3 checklist: A brief, self-rated screen for depressive, bipolar, anxiety, and post-traumatic stress disorders in primary care. Annals of Family Medicine. 2010;8:160-169. ↩
47. American Academy of Family Physicians. Clinical preventive service recommendation: Sexually transmitted infections. http://www.aafp.org/patient-care/clinical-recommendations/all/stis.html. Updated 20152015. ↩
48. American Academy of Family Physicians. Summary of recommendations for clinical preventive services. http://www.aafp.org/online/en/home/clinical/exam.html. Updated 2009. Accessed 12/10/09, December 10, 2009.
49. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: A systematic review. J Med Internet Res. 2015;17(2). ↩

Copyright: © 2017 Martha Staeheli, Robert Aseltine, Nicholas Warren, Nicole Seagriff, and Bruce Gould. Published here under license by The Journal of Participatory Medicine. Copyright for this article is retained by the authors, with first publication rights granted to the Journal of Participatory Medicine. All journal content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License. By virtue of their appearance in this open-access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.