To Text or Not to Text? That is the Question

Abstract

Texting is ubiquitous with a text frequency of 145 billion/day worldwide. This paper provides partial results of the national demonstration project called the Missouri Quality Improvement Initiative (MOQI). MOQI goals were to reduce avoidable hospitalizations using APRNs to infuse evidence-based practices, model appropriate decisions and improve communication among workers responsible for nursing home resident care. This is a retrospective content analysis of text messages sent and received via a secure, password protected, encrypted mobile text message platform called Mediprocity. Text messages were created by 15 APRNs and a PhD-RN project supervisor working in 16 nursing homes over 6 months (January 1-June 30 2018). During the 6 months of data collection 8,946 text messages were captured, coded and analyzed. Data included 1,018 sent messages and 7,928 received messages. The most common messages sent (n=324) and received (n=2319) were about patient updates. The second most common texts included messages confirming information (n=1312).

Introduction

The World Health Organization¹ has promoted mobile health (mHealth) as a mechanism to transform health service delivery across the globe for many years. In 2005, the World Health Assembly adopted a resolution establishing an eHealth strategy for WHO¹. In this resolution, eHealth was defined as, “…the cost-effective and secure use of information and communication technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education”². In 2011, WHO recommended a more specific definition of mHealth defined as any “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices”³. In 2019, WHO released its first guideline for digital health interventions⁴. The guideline gives recommendations on nine prioritized digital health interventions including targeted client communication via mobile devices, health worker decision support via mobile devices, and digital tracking of clients’ health status and services combined with decision support⁴. The purpose of this research was to analyze the content of secure messages that include patient health information (PHI) as clinical staff use mobile device technology (secure encrypted mobile texting using health information exchange via I-phones) who were caring for chronically ill nursing home residents. All methods for this research were approved by the University of Missouri Institutional Review Board (protocol #1203541).

Background

This research was part of a larger national demonstration project in nursing homes with seven sites in the United States (U.S.)⁵. The details of this paper provide partial results of the national demonstration site known as the Missouri Quality Improvement Initiative (MOQI). Overall goals of the MOQI project were to reduce avoidable hospitalizations and reduce hospital transfers in 16 nursing homes using APRNs to infuse evidence based practices and to model appropriate decision making and communication strategies to healthcare workers responsible for care of chronically ill long-stay nursing home residents6. Project interventions to reduce transfers included embedding APRNs full-time in 16 Midwestern nursing homes⁷, using INTERACT quality improvement tools to standardize documentation and early identification of change in resident condition⁸, having advance care planning discussions to identify goals of care⁹, and implementing secure health information exchange technology to support safe communication about resident change in condition among healthcare workers using secure messaging systems¹⁰. This research focused on findings from a content analysis of secure texts retrieved from a health information exchange technology intervention used by nurses to communicate about resident care. Project nurses carried and used mobile phones to access the secure network to share vital PHI via text messages with clinical partners about nursing home residents experiencing a change in condition. Research about the use of secure protected text messaging and content is innovative. There have been no studies, to our knowledge, completed about content of secure text messages in nursing homes.

Technology in Nursing Homes

In 2017, health information technology was ranked as the fourth major issue out of 10 facing the future of long term care, including nursing homes¹¹. In recent years, adoption of health information technology by nursing homes has begun to increase in frequency with greater capabilities, extent of use, and integration. Near the beginning of this century, aged care experts began envisioning health IT as a benefit for research and clinical practice. In 2001, Bowles et al.¹² implemented a web-based research information system designed to efficiently measure health related quality of life measures in several aged care sectors including home care, nursing homes, assisted living, and the Post-Acute-Care for the Elderly (PACE). At this same time, web browsing and broadband communications were coming into vogue and began to be used for early telemedicine activities, including teleconsultations used for communication between and among healthcare professionals in homes for elders¹³. The introduction of an electronic health record (EHR) in aged care settings escalated research investigating initial implementation strategies¹⁴, existing infrastructures¹⁵, barriers and facilitators to adoption of EHR¹⁶, and the impact EHR implementation has had on outcomes such as costs, staffing and quality indicators¹⁷. The introduction of EHR sparked implementation of data systems that supported enhanced clinical decision support at the bedside¹⁸, clinical systems supporting care delivery activities such as medication administration¹⁹, laboratory results reporting²⁰, referral and documentation²¹, and administrative systems used to improve patient safety²². Facilitators of health information technology adoption in nursing homes (e.g. health information exchange) included workflow integration, enhanced communication, increased effectiveness of care, and patient safety²³. Few research studies, if any, have examined the influence of mobile technology with secure health information exchange that incorporate PHI in nursing home settings. This research provided evidence to build knowledge about how secure mobile texting platforms are used by nurses to communicate nursing home residents’ change in condition to prevent hospital readmissions. Studies like this are needed to help identify the risks to patients and staff who are using text message systems to communicate PHI and to infer methods of secure texting use, such as recommended structures of priority areas in the content of text messages.

Methods Design

This research is a retrospective content analysis of text messages sent and received via a secure, password protected, encrypted mobile text message platform called Mediprocity²⁴. We define content analysis as, “a research technique for the objective, systematic and quantitative description of the manifest content of communication.”²⁵ During this project, Mediprocity owners, who were strategic partners with MOQI, provided a free service that allowed licensed nurses to use a secure electronic platform to exchange PHI via text messages. The research team collaborated with Mediprocity leadership to offer APRNs, hired as part of MOQI, secure connections to the health information exchange network. Members of the research team extracted text messages created by 15 APRNs and a PhD prepared RN project supervisor on MOQI working in the 16 nursing homes over a period of 6 months (January 1-June 30 2018). Text messages were extracted by members of the research team from a repository of all stored text messages sent and received by providers during the period.

Subjects

Nursing Homes. Sixteen nursing homes participated in the MOQI, ranging in size from 120 to 321 beds, and located in urban, metro, and rural communities within 80 miles of a large Midwestern city. To be eligible, residents had to be in the nursing home more than 100 days, with a traditional Medicare and/or Medicaid fee-for-service payer²⁶.

Nurses. APRNs were hired as part of MOQI to help achieve the goal of reducing hospital readmission rates in 16 nursing homes. Sixteen nurses, including 15 APRNs and 1 PhD RN Project Supervisor, agreed to use Mediprocity, were trained, and provided a software account. There was a wide range of experience of the 16 MOQI nurses both as RNs and APRNs. The 16 RNs had .5-44.7 years of experience, 15 RNs were licensed APRNs with .8-30.9 years of experience.

Procedures

Mediprocity implementation. Mediprocity is a HIPAA-compliant communication and clinical collaboration tool primarily designed to be used amongst providers, pharmacies, and prescribers. The platform may also be used to communicate with outside vendors, patients and anyone looking to communicate PHI in a secure environment that meets the National Institute of Standards and Technology as well as Health Information Technology for Economic and Clinical Health Act^27,28. Mediprocity meets the standards recommended by the Office of Civil Rights when it comes to a mobile first policy for lock/logoff functions, authentication, encryption, remote wipe and retention of messages.

The Mediprocity platform may be accessed using a browser on a desktop or mobile device or by installing a mobile application or Windows 10 widget. The system allows for additional workflow features specifically designed for the long-term care/post-acute setting such as read receipt with average response time tracking, adding and removing users to a message, multi-use account with shared names for identification and auto-escalation.

Mediprocity has always been free for any prescriber (Medical Doctor, Doctor of Osteopathy, Nurse Practitioner, and Physician Assistant). Mediprocity is a robust tool that can integrate into any system such as telephone answering services or EHR. The secure forms system can be used to pass secure advance directives and admission information that require data input with binding signatures.

Smart phone implementation and APRN training. The APRNs and Project Supervisor were supplied smart phones through the grant. These staff were able to choose between an Android or an iPhone (specifically Samsung Galaxy S7 32GB or iPhone 7 128GB). All phones had PIN security and met the compliance requirements of the University of Missouri.

All APRNs and Project Supervisor required to use phones were assisted by the project Health Information Coordinator in creating their Mediprocity account and associating it under the University of Missouri organization for local administration purposes. Once the account was created, the user was provided training on the secure texting functionality. This included searching for collaborating health care providers and support personnel in the directory, sending connection requests to these collaborators, sending and receiving a test message, setting notification preferences and reviewing other setting options. Assistance was also provided regarding establishing a process for the use of secure texting and updating an electronic communication policy for each nursing facility.

Health information technology support. Once a user created their account, the Health Information Coordinator provided technical support for the APRNs and Project Supervisor. This support included password and encryption key resets, helping set up new collaborating providers, providing an additional layer of support for the users in the nursing homes, troubleshooting technical issues, reviewing current processes and policies, and removing and deactivating accounts as needed. Additionally, when there were improvements or new functionality available, this information was communicated to the APRNs, Project Supervisor, and participating nursing homes by the health information coordinator.

Analysis

Six months of text messaging content were downloaded from Mediprocity into an Excel database to begin the coding process. Data were organized by APRN and Project Supervisor into separate tabs within the Excel database. All text messages sent and received by individual APRNs were coded separately. Organizing coding methods in this manner, by keeping APRNs text messages separate, enabled the primary coder to build the codes across all APRNs and Project Supervisor to contrast the purpose and meaning of text messages, to maximize potential variation of codes, and to differentiate sequences of how text messages were constructed among the project nurses during communication.

Two research team members with experience in nursing homes, collaboratively coded the text message data. The coding editors were responsible for creating, updating, revising, and maintaining the master code list for the research team. A process of member checking was conducted regularly as coding progressed to enhance trustworthiness. Member checking included sharing coded field note excerpts and discussing coding quandaries as coding categories emerged. Member checking also enabled the team to generate peer support and to find better connections between categories through a process of interpretative convergence²⁹.

Results

During the 6 months of data collection content from 8,946 text messages were captured and analyzed. Data included 1,018 sent messages and 7,928 received messages. The most frequent discussions were between licensed practical nurses (LPNs), RNs, other APRNs, and physicians. There was wide variability in the use of Mediprocity to send (range 0-607) and receive (0-7358) text messages (See Table 1). For example, one APRN (Nurse 1) had a high number of text messages received, this was possible because she expected to be included in text message communications about resident care, whereas other APRNs had mechanisms of communicating with staff that were not always electronic. Additionally, there was wide variability in leadership responses to using mobile cell phone technology. There were varying degrees of enthusiasm among leadership in these facilities regarding text message implementation. Leadership in the facility with the APRN with high use was very enthusiastic and encouraged mobile technology use, including embedding it in communication processes used by staff.

Table 1:

Total Text Messages Sent/Received

	Sent	Received
Nurse 1	607	7358
Nurse 2	9	6
Nurse 3	16	13
Nurse 4	41	29
Nurse 5	131	140
Nurse 6	1	2
Nurse 7	106	119
Nurse 8	10	10
Nurse 9	1	0
Nurse 10	22	140
Nurse 11	1	22
Nurse 12	1	1
Nurse 13	0	3
Nurse 14	70	74
Nurse 15	1	2
Nurse 16	1	9
Total	1018	7928

Table 2 illustrates the individual codes and their descriptions found in the content of text messages. The most common messages sent (n=324) and received (n=2319) were about patient updates (P-1) (including vital signs (VS)). The second most common text included messages seeking to confirm information (C-1) (n=1312). One of the top five messages included when someone was added to the conversation (A-1) during a text message exchange and occurred most frequently after messages were received. Examples of the codes with verbatim deidentified text message statements are illustrated in Table 3.

Table 2:

Codes, Code Descriptions, and Total Messages Sent/Received

Code	Meaning	Total Messages Sent	Total Messages Received	Total Messages
P-1	Patient Updates (e.g. Vital Signs (VS))	324	2319	2643
C-1	Confirmation of Information	156	1156	1312
P-2	Questions about Patients/Questions	142	880	1022
C-2	Clerical Information	113	858	971
A-1	Added to Conversation	35	786	821
O-1	General Orders	76	726	802
A-2	Automated Reply	142	235	377
LCMP	Lab Results Complete Metabolic Profile	3	152	155
O-4	Other Medication Orders	4	151	155
LST	Lab Results Specific Test	0	134	134
LCBC	Lab Results Complete Blood Count	0	119	119
LUA	Lab Results Urinalysis	2	108	110
O-2	Antibiotic Orders	5	82	87
RCXR	Radiology Results Chest Xray	2	77	79
LBMP	Lab Results Basic Metabolic Profile	0	42	42
C-3	Staffing Information	11	23	34
LH	Lab Results Hemaglobin	0	19	19
RF	Radiology Results Fracture	0	18	18
O-3	Pain Medication Orders	0	11	11
O-5	Intravenous (IV) Fluid Orders	0	9	9
EKG	Electrocardiogram Results Sent	0	6	6
RABDM	Radiology Results Abdomen	0	4	4
HC	Hospice Care	1	3	4
RD	Doppler Results Sent	0	3	3
RCT	Radiology Results computed tomography	0	2	2
KUB	Kidney Ureter Bladder Results	0	1	1
MIS	Miscellaneous	2	4	6
Total		1018	7928	8946

Table 3:

Coded Examples of Text Messages Sent/Received via Mediprocity

Code	Descriptor	Text Message Sent	Text Message Received
P-1	Patient Update sent with clarification and update received	P-1: “Hi XXX, not sure you know about XXX went to hospital again on the 20th and back on 22, thought to be due to muscle relaxant. Same symptoms.	P-1: I thought you meant she went on the 20th and then went again on the 22nd...Im following now! I saw her Friday and she was better and the muscle relaxer was dc’d.
P-1 with C-1	Patient Update received with Confirmation of Information sent	P-1: “ok”	C-1: “I will be sending over death documentation. No s/sx [signs/symptoms] of life, unable to obtain v/s [vital signs] 12:20pm. On XXX hospice. They are on their way in.”
P-2 with C-1	Questions about Patients/ Clinical Questions sent with Confirmation of Information received	P-2: “Can we dc [discontinue] guaifenensin bid [twice a day] was from last July when on Hospice for copd?”	C-1: “Yes”
C-2	Clerical Information sent with no confirmation	C-2: “script for XXX needs signed 5/325 is Dr. XXX
C-2	Clerical Information sent,	C-2: “XXX wants me to check his ears I may not get to it. I have the meeting downstairs tomorrow so I won’t be here tomorrow.” C-2: “Now they tell me she has an ENT appt [Ear Nose and Throat appointment] on Thursday. I just saw him Friday.”	C-2: “Okay, I don’t know if I will get to them either, I still have more pts to see for regular visits”
O-1	General Orders received		O-1: “Hi ladies, I got ahold of Dr XXX and she was okay with labs, CXR, and ordered routine nebs TID [nebulizers three times a day] x 3 weeks
P-1, RCXR, O-1	Patient update sent, patient update received, nursing orders received	P-1: “XXX I apologize that we did not send an assessment or vitals with our request for a CXR yesterday. XXX our nurse today will give you an updated assessment complete with vitals and the CXR results. We recently had to do a prednisone taper on her. Based on the CXR results you are about to receive we should consider doing another round. She is still wheezing thru out with a dry cough. The last few times I checked O2 she’s been running 92-93%”	P-1: “VS 97.0 - 142/82 - 90 - 18, bilateral rhonchi through out with non productive cough” RCXR: “Radiology results sent” O-1: “Yes, we can restart the Medrol Dose Pack and what was the previous course of antibiotics?” P-1: “Levaquin 750 1 daily X 10 days was previous”
	Patient update received, order received, confirmation received, patient question received, CBC lab results sent confirmed.		P-1: “The above mentioned resident was laying in bed and was found sitting on floor. It was an unwitnessed fall therefore neuro checks have been initiated. No injuries noted. ROM WNL [Range of Motion Within Normal Limits]. BP-104/52, P-64, R-16, O2-98, T-98.3. May we please have an order to obtain a CBC along with a UA as resident is prone to UTIs [Urinary Tract Infections]?” O-1: “Yes. W. C&S [Culture and Sensitivity]” C-1: “will do thank you” P-2: “Awaiting UA. Any orders as of now?” LCBC: “Lab results CBC sent”

Table 4 provides details of the top five classifications and total sent and received text messages by the nurses in this study. The sent and received messages of the top five classifications comprised 75.7% (6775/8946) of text messages evaluated during this period. Patient updates included 26% of the messages sent and received. Nearly 88% of patient updates were received by APRNs versus only 12% sent by the same nurses. The second most frequent classification was confirmation of information at 14.7% (1312/8946) of all text messages. Most confirmations of information were sent by APRNs, 86.2%.

Table 4:

Top Five Classifications of Sent and Received Text Messages by APRN^*

	Total Messages		Patient Update		Clerical Information		Confirmation of Information		Questions/Questions about patients		Added to Conversation
Nurse	Sent	Received	Sent	Received	Sent	Received	Sent	Received	Sent	Received	Sent	Received
1	607	7358	90	2200	69	729	85	1004	105	804	25	766
2	9	6	4	0	0	0	4	0	1	0	0	0
3	16	13	8	2	0	2	4	3	4	0	0	0
4	41	29	21	2	0	0	4	8	10	7	4	0
5	131	140	79	26	9	10	27	47	14	22	2	7
6	1	2	0	0	0	2	1	0	0	0	0	0
7	106	119	64	19	13	43	19	37	5	16	4
8	10	10	5	2	2	6	2	1	1	1	0	0
9	1	0	0	0	1	0	0	0	0	0	0	0
10	22	140	7	51	2	30	5	25	1	31	1	4
11	1	22	0	1	1	9	0	3	0	0	0	8
12	1	1	0	0	1	0	0	1	0	0	0	0
13	0	3	0	0	0	3	0	0	0	0	0	0
14	70	74	46	10	13	19	5	25	1	9		2
15	1	2	0	0	1	2	0	0	0	0	0	0
16	1	9	0	1	1	1	0	2	0	0	0	1

^*Totals do not add up to previous numbers because this table includes only the top 5 classifications

Discussion

Texting is used everywhere with an estimated text message frequency to be 145 billion per day worldwide³⁰. In 2014, American adults sent and received an estimated 32 texts per day³⁰. In healthcare, texting to share PHI is also becoming a ubiquitous resource used for many purposes, for example, for post ambulatory care discharge follow-up³¹, chronic illness management³², and cardiac rehabilitation in coronary heart disease patients³³. Authors are unaware of any other studies that have examined content of secure text messages in nursing homes. This research provides evidence that texting is a critical process that is used often in clinical communication and should be examined in nursing home settings too.

In this sample, wide variability of use in numbers and types of messages sent and received and content were found. A possible explanation for variability in numbers and type of messages sent and received could be due to different staff perceptions of how essential it was to send and receive information via an electronic text message system. Staff perceptions could be influenced by factors such as vision of leadership; policies, or lack of, guiding use of text-based messages; perceived benefits and disadvantages of using the text-based messaging system. Similar explanations have been found in other studies examining cross-sectoral text-based communication between hospital and home care nurses³⁴.

There was a good amount of variability to the structure of the text message content in this sample. For example, verbatim text messages in Table 3 illustrate the randomness of some message content compared to others including organization of content, abbreviations, tone of the message. These characteristics may influence the ability of staff to prioritize message contents, provide timely and appropriate response, and/or exhibit trustworthiness in the information conveyed within the messages. These characteristics could also contribute to the large amount of confirmations (n=1312, see Table 2) in this sample to assure accuracy of information that were sent and/or received by nurses. Similar conclusions were offered in a study of an electronic messaging system used for medication administration in community dwelling home care patients³⁵. For example, home care nurses using the electronic messaging system would make extra phone calls to double check the accuracy of medication lists in messages that they did not trust³⁵.

Crucial information exchange is taking place with text messages reviewed in this study. Oftentimes vital information is shared in a snippet of clinical information such as conditions changes, verbal orders, medication changes, etc. There have not been studies that demonstrate how often this type of health information exchange is being captured as part of the electronic medical record history. Within these 16 facilities, there was wide variability in thought among leadership about what texts to store and record including timing and frequency of storing and recording messages as part of the permanent electronic medical record. Policy recommendations for texting in healthcare can address these administrative and physical precautions through established consenting processes, digital security plans, procedures for storing and deleting messages, directives on message content including length of messages (e.g. 160 characters), and best practices related to timeliness of response³⁶. Rigorous research studies are badly needed to determine the constancy of how text messages are used in healthcare across multiple facilities and staff using this capability.

Limitations

Out of network, users that are not Mediprocity users, are not captured in the exchange of information unless the out of network user sends a message to the Mediprocity user. Therefore, there is an incomplete view of health information exchange in these facilities using secure texting as a resource. Lack of complete information could make an evaluation of text-based messaging systems very difficult creating confounding variables that are hard to control for in a rigorous study. However, that does not negate the importance of evaluating these IT capabilities and their impact on quality and safety in healthcare systems allowing their use. An additional limitation is that most of these text messages were representative of one nurse, limiting generalizability of findings. However, this finding does strongly suggest that we need to explore why nurses or administrators are reluctant to use secure text messages to share PHI content via text messages.

Conclusion

It is critical to understand how emerging technologies are impacting the work of healthcare providers in the workplace. Accessing clinical information through a mobile health IT application using secure text messages via a phone would seem to have many benefits. However, these mobile systems can have many pitfalls, such as variability of content and structure, leading to unintended consequences, possibly creating threats to patient safety and quality, if the system processes are not managed well. Rigorous mixed methods research is needed to determine how these mobile systems are impacting care delivery, safety, and patient outcomes.