Clinical Surveillance, A Concept Analysis: Leveraging real-time data and advanced analytics to anticipate patient deterioration. Bringing theory in practice.

CITATION:

Jahrsdoerfer, M. (Winter 2019). Clinical Surveillance, A Concept Analysis: Leveraging real-time data and advanced analytics to anticipate patient deterioration. Bringing theory in practice. Online Journal of Nursing Informatics (OJNI), 23(1), Available at http://www.himss.org/ojni

Introduction

When nurses express how patient care is delivered, they use words such as “monitoring,” “critical thinking,” “observation,” and “intervention” interchangeably to describe clinical surveillance.
This concept analysis will define and clarify the core and operational meanings of clinical surveillance using a clear and systematic methodology; examine the integral role clinical surveillance plays in healthcare technology integration; and explore how clinical surveillance impacts patient care and outcomes.

In today’s acute-care environment, multiple internal and external factors give clinical surveillance characteristics distinct from those other terms and have changed how nurses apply critical thinking and decision-making to patient care and safety. This concept analysis will begin by identifying those internal and external factors.

Clinical surveillance: internal factor characteristics
Technology. Clinical surveillance utilizes multi-variate, continuous, real-time data from multiple monitoring devices; applies advanced analytics to provide a quantitative and qualitative estimate of a patient’s condition over time; and communicates clinically relevant alerts to the appropriate clinician.

Workflow. Within the context of workflow, clinical surveillance is a systematic, goal-directed process that trends physiological changes in patients, interprets the clinical implications of those changes, and alerts clinicians for timely interventions (CDC, 2018; Giuliano, 2017, p. 34).

Professional. The Nursing Interventions Classification (NIC) defines surveillance as “purposeful and ongoing acquisition, interpretation and synthesis of patient data for clinical decision making” (Butcher, Bulechek, Dochterman & Wagner, 2018, p. 632),

Dougherty’s seminal work defines a nursing intervention classification (NIC) of surveillance as “the application of behavioral and cognitive processes in the systematic collection of information used to make judgments and predictions about a person’s health status” (Butcher et al., 2018, p. 524). A principal interpretation infers that if a specific clinical parameter of interest requires monitoring, then nursing care is usually not required (Butcher et al., 2018, p. 530).

Clinical surveillance: external factor characteristics

While Dougherty’s definitions share similar or overlapping characteristics, an exacting definition of surveillance remains elusive. This makes it difficult for health systems to respond to growing external pressure from numerous healthcare agencies and advocates recommending broader use of clinical surveillance practices. For example:

  • The Joint Commission (TJC): In its July 2017 newsletter, TJC recommended that hospitals “monitor the use of opioids to determine if they are being used safely” (p. 1) and included the tracking of adverse events, such as opioid-induced respiratory depression (OIRD).
  • Centers for Medicare and Medicaid Services (CMS): In March 2014, CMS issued a proposed quality measure that recommended that hospitals "at a minimum have adequate provisions... for post-operative monitoring of patients receiving IV opioid medications, regardless of where they are in the hospital” (p. 1). This came on the heels of a 2013 quality measure (#3040) recommending that “monitoring needs to be ‘documented’ and the time between documentation must ‘not exceed 2.5 hours,’” which was criticized as insufficient for adequately protecting patients from adverse events (Wong, 2014).
  • Anesthesia Patient Safety Foundation (APSF): According to association president Robert Stoelting, MD, the “APSF recommends that monitoring be continuous and not intermittent and that continuous electronic monitoring with both pulse oximetry for oxygenation and capnography for the adequacy of ventilation be considered for all patients” (CMS, 2014, p. 1).
  • TJC: According to High Reliability: The Path to Zero Patient Harm (DuPree, 2016), a root challenge lies with frequent nursing interruptions affecting the consistency and reliability of patient care. Complexity of disparate devices warrants consistent integration of the right information to be delivered to the right person at the right time.

 
For reasons associated with technology advances, clinical workflow efficiencies, and external pressures, the fundamental definition of clinical surveillance must be re-evaluated and standardized to bring clarity to both its meaning and implications.

The American Association of Critical Care Nurses (AACN) Synergy Model of Patient Care supports the theory that “synergy results when the needs of a patient and characteristics of a clinical unit or system are matched with a nurse's competencies” (Hardin & Kaplow, 2017, p. 6).

In Curley’s development of the AACN Synergy Model, this synergy is expressed as “patients’ needs and system characteristics [that] drive nurse competencies; when the two are in synchrony optimal patient outcomes result” (Hardin & Kaplow, 2017, p. 6).

The AACN Synergy Model serves as a theoretical framework in which to understand the concept of clinical surveillance in the acute-care hospital environment. The explicit intent of the Synergy Model addresses relevant aspects of nurse-patient and nurse-system relationships.

To take this theory one step further, it only holds clinical value if the relevant fluid streams of data points are analyzed and communicated appropriately—meaning, the right data, to the right person, at the right time.

This concept analysis introduces technology into the clinical surveillance model of care. Until this point, all concept analyses conducted on clinical surveillance focused exclusively on the nursing role. However, advances in technology permit the proactive analysis of trends that can capture a sentinel event before it occurs. Thus, it is imperative that these components be added to the concept of clinical surveillance.

II. Concept selection
The first step in a concept analysis is to identify the concept itself. The analysis selected for exploration is clinical surveillance.

III. Purpose
The purpose of a concept analysis is to clarify the meaning of a specific concept of interest through the use of a clear methodology or strategy. The Walker and Avant (2019) concept analysis method is used here because of its straightforward approach and scrutiny. A concept is usually one or two words that convey meaning, understanding or feelings between or among individuals within a same discipline.

IV. Aim
Examine clinical surveillance’s integral role in the integration of healthcare information technology (HIT) and how it subsequently affects patient care and outcomes.

V. Methods
The purpose of a concept analysis is to convey a term’s core meaning through deliberately and systematically simplifying both theoretical and practical use models. The intention of this concept analysis on clinical surveillance (as it relates to prevention of an adverse outcome) is to refine ambiguous meanings of the concept itself. Walker and Avant’s process of analysis was used to help clarify the concept of surveillance.

Once the process of examining the basic elements of the concept has been exhausted, an operational definition of clinical surveillance will ensue, that “by its very nature increases the validity of the construct, that is, will accurately reflect its theoretical base” (Walker & Avant, 2019).

In an effort to capture the essence of the concept of clinical surveillance, the eight-step procedure indicated by Walker and Avant (2019, p. 46) has been followed: 1) select a singular concept; 2) determine the purpose of the analysis; 3) identify all uses of the concept discovered; 4) determine the defining attributes (inherent characteristics); 5) identify a model case; 6) identify a borderline, related and contrary case; 7) identify antecedents (event or activity that immediately precedes a problem behavior) and consequences (event that immediately follows a response); and 8) define the empirical referents which are “categories of actual phenomena that by their existence or presence demonstrate the occurrence of the concept itself.”

A global search was initiated using the electronic databases of PubMed, CINAHL, MEDLINE, OVID, and Google Scholar between the years 1972 to 2018. The search contained the following terms: surveillance, patient-safety, clinical alarms, clinical communication, concept analysis, acute care, electronic health records, nursing practice.

A secondary literature search was conducted using pertinent reference listings of the articles identified by the first search. Thirty-four journal publications, four textbooks, two dictionaries, and one thesaurus were used for the analysis.

To demonstrate the theoretical meaning of surveillance to nursing practice, the AACN Synergy Model of Patient Care was used to place the concept of surveillance into a contextual framework of patient safety in the technology-driven, acute-care hospital setting (Hardin & Kaplow, 2017).

VI. Findings

Dictionary and thesaurus definitions
Initial information about surveillance was identified in dictionaries and a thesaurus. The online version of Merriam-Webster dictionary (Merriam-Webster, retrieved 2018) search of surveillance lists both French and Latin derivatives—from the French surveiller to “watch-over”, sur + veiller; and from the Latin vigilare, from vigil “watchful”–“at vigil.” Its definition is listed as “continuous observation of someone or something” –“as by detective” or “supervision.” According to Thesaurus online, synonyms for surveillance are: attention, observation, vigilance, scrutiny, citadel, scan, safeguard, recognition, examine, monitor, measure, analyze, ascertain, track, spy and espionage.

Uses of the concept
When conducting a true analysis of a concept, the core meaning of the concept itself must prove true outside of the discipline being studied. In other words, an overview of how and where this concept has been used in a real-life setting, other than healthcare, must be explored. Its core meaning will be the same, no matter the domain. Therefore, the next section will briefly highlight alternative contexts outside the acute-care hospital setting. In demonstrating various uses of the concept, its core meaning should always remain the same.

Alternative Uses of the Concept of Surveillance

Military and intelligence usage of surveillance. The early 1940s saw the emergence of sophisticated reconnaissance military aircraft. The mission of this aircraft was to secretly scout, survey and collect information about enemy movements and position, then communicate the information back to the control base (Piehler & Johnson, 2013).

The National Aeronautics and Space Administration (NASA) used reconnaissance aircraft to carry out surveillance at high altitudes, continuously sampling enemy data in space (Watts, Ambrosia & Hinkley, 2012).

Over the decades technology became more sophisticated, allowing for small unmanned aerial vehicles (SUAV). Of particular interest was the AeroVinronment RQ-11. Its intuitive use is to gather intelligence or reconnoiter a location, with the intent of communicating intelligence (Alex, 2018).

There are many more military examples of surveillance, from submarines to drones, all with the same general attributes used in healthcare: attention (on a specific data point), timeliness (prospective prevention of adverse outcome), recognition (of incongruity), action (ability to capture data via algorithm, photos, video), intuitiveness (to work behind the scenes and function unnoticed), analysis (accumulate data and draw conclusions), and collaboration (disseminate vital information back to the source).

Legal-law-private-social uses of surveillance. Yet another domain in which surveillance is actively prominent is within the legal, law enforcement, private and social settings. The realm of legal and law enforcement typically involves the use of video surveillance and facial recognition. Surveillance cameras fill our city and suburban streets. This networked maze of computers provides real-time data, allowing police to foil and prohibit an event before it escalates (Ferguson, 2017). Surveillance cameras, especially since 9/11, are used in countless public places—court houses, parking lots, shopping malls, alleyways, traffic lights, sporting venues, hospitals and concert halls—all with the primary purpose of recognition, deterrence, identification, communication and protection.

Private and social uses of surveillance include surveillance cameras outside and inside homes, private investigators, and location tracking apps. All of these use-models embrace the same attributes as clinical surveillance. Within the legal, law enforcement, private and social uses of surveillance, the defining attributes are attention (target explicit data point), timeliness (real-time vs. retrospective), recognition (of unusual behavior or trends), action (ability to capture the data), intuitiveness (to be inconspicuously vigilant), analysis (accumulate data and draw conclusions), and collaboration (communicate vital information in real-time with others).

Public health usage of surveillance. Public health surveillance is defined as the “continuous systematic recognition, collection, analysis, interpretation and dissemination of data about a health-related event for the use of public health action to reduce morbidity and mortality and to improve health” (CDC, 2018, p. 1).  Specifically, the Centers for Disease Control and Prevention (CDC) serves eight different public health surveillance functions, including the early and timely detection of outbreaks; health intervention; estimating the impact of disease or injury; reports on the natural history of a health condition; identifying the distribution of an illness, which involves the ability to recognize patterns; research; prevention and control; and planning.

The term surveillance, in a public health context, is classified as either active or passive surveillance (CDC, 2018). Essentially, active surveillance involves prospective steps to identify patients who may be at risk for a disease, whereas passive surveillance relies on retrospective or secondary data sources to monitor community health, such as hospitals, clinics and public-health offices (WHO, 2018). Active public-health surveillance shares all of the same attributes of clinical surveillance: timeliness, attention,
recognition, intuitiveness, analysis and collaboration. Passive public health surveillance holds all the attributes except timeliness.

Acute patient-care usage of surveillance. According to the Nursing Interventions Classifications (NIC) #6650, surveillance is defined as “purposeful and ongoing acquisition, interpretation and synthesis of patient data for clinical decision making.” When the dimension of safety is added to surveillance, NIC defines it (#6654) as “purposeful and ongoing collection and analysis of information about the patient and the environment for use in promoting and maintaining patient safety” (Butcher et al., 2018, pp. 367, 170 respectively).

The existing definition of surveillance addresses the “ongoing acquisition” of patient data, which has been adequate until now. Rationalization of “ongoing acquisition” likely infers “periodic acquisition” of data, whereas the current definition of clinical surveillance warrants “continuous acquisition” of data. The modification assures the data-gap closure, allowing the integrity of the data collection to be pure in its totality.

Accomplishing today’s daily nursing work without taking advantage of healthcare’s advances in digital and computer capabilities would be impossible and impractical. Without assimilation of continuous data acquisition into the concept of clinical surveillance, the nurse lacks the ability to assimilate all available data. The sheer volume of data collected from patients is beyond the capacity of the human brain to analyze. Identifying the complex relationships across data sets is impossible without computers. In essence, without continuous real-time data and rules, the nurse alone lacks proficiency to deliver safe patient care.

All other uses of the term surveillance outside the domain of healthcare incorporate timeliness as a defining attribute. The difference between all previous definitions of clinical surveillance and the current operational definition of surveillance is the delineation of real-time data, defined as the attribute of timeliness.

Many contemporaries in the field have judiciously examined the concept of surveillance from a multi- faceted nursing perspective (Milhomme, Gagnon & Lechasseur, 2018; Heslop & Lu, 2014;
Kelly & Vincent, 2011; Mohammadipour, Atashzadeh-Shoorideh, Parvizy & Hosseini, 2017; and Pfrimmer et al., 2017).

All these works address vigilance, attention, collaboration, processes, evaluation, intervention, synthesis, analysis, safety and action in one form or another. However, the theme of the timeliness of technology has not been articulated.

Nurses rely on real-time data from technology in everyday work. With clinical surveillance being central to their core responsibilities, an accepted updated version must serve as a standard of reference, including this pivotal attribute of clinical surveillance. The key differentiator argued here is the inclusion of prospective data to the concept versus retrospective data. Critical care units in hospitals were established for the purposes of monitoring signs and symptoms to be able to intervene quickly in life-threatening situations.

The primary goal of intensive care is to “prevent further physiologic deterioration while the underlying disease is treated and resolves” (Marshall et al., 2017, p. 1). While this an accurate statement, what are clinicians doing on the medical-surgical floors to prevent escalation to this tertiary level of care? While patients receiving critical care warrant constant surveillance, should not certain situations on the medical- surgical floor be sanctioned for the same level of care? A prime example of this would be a post-operative patient at risk for opioid-induced respiratory depression (OIRD) receiving a controlled analgesic. The clinician interpreting the data is only as good as the timeliness of the system-data.

Benner’s (1984) seminal work Novice to Expert describes five levels of proficiency among nurses: novice, advanced beginner, competent, proficient and expert. Within the context of a potentially critical patient situation, we understand that nurse proficiency is a key attribute of clinical surveillance. The AACN Synergy Model states that patients’ needs drive nurse competencies, and nurse competency is affected by system technology. “Increasingly, hospitals are charged with treating sicker patient populations, even outside the ICU” (Welch, 2015, p. 1). As demonstrated in the “Related” case study (see Case studies section), we have a novice nurse, whose nursing genesis of experience and intuition has yet to develop. Lacking some competencies, in the infancy of her nursing role, the nurse received notifications via the technology utilization of continuous clinical surveillance, resulting in a clinical team assembling to address a patient with Opioid-Induced Respiratory Depression (OIRD). Missing one or more of the defining attributes of clinical surveillance may not result in a textbook response; nevertheless, system proficiency serves as necessary support in the midst of complex patient care.

How can nursing leaders expect professional primary nurses to meet patients’ needs and perform their work proficiently if the system provides only fragmented, episodic data to work with? Nurses must have real-time, continuous data flow and analysis for accurate trending to occur. Surveillance infers that no clinical anomaly channels the acknowledgement/intervention filter; in this case, the resources are knowledge and technology.

When attempting to get the patient from point A (admission) to point B (discharge), there must be a safety net in place vis-à-vis medical device integration (all measurable clinical data sources) for the nurse to exercise competent judgement and decision-making. The objective is to capture all of the continuous patient data and provide the analysis so that trends can be identified, with the end goal of preventing an adverse patient event. The contrary case results in gaps in data due to siloes or data redundancy. Planning for optimal safe patient care warrants a checklist of attributes present when declaring that an institution uses clinical surveillance.

VII. Defining attributes of surveillance
The third step in the concept analysis is to list the defining characteristics or common attributes of the concept (Walker & Avant, 2019). The defining attributes of the concept of clinical surveillance in the literature include: (see table 1 for definitions)

  • Attention
  • Timeliness
  • Recognition
  • Intuition
  • Analysis
  • Action
  • Collaboration

Antecedents and consequences of surveillance
Antecedents are events that must be present or have occurred prior to the manifestation of a concept. The consequences are those events that occur as a result of the concept itself. The process of identifying both the antecedents and consequences of a concept can help us to understand the context in which the concept is generally used (Walker & Avant, 2019). An antecedent may not be the same as a consequence for the same concept.

Antecedents
The antecedents of clinical surveillance are as follows:

  • The patient must be present and is the sole focus of surveillance.
  • Expert individual in field (acute care): This is the nurse responsible to watch over or carry out

surveillance. Specifically, as the expert, the nurse should not miss (due to siloes of information) or dismiss (due to excessive and redundant information) an actionable alarm and/or event.

  • Object, data, information or in this case symptoms and/or diagnostic results that warrant attention related to a person, in this case the patient-clinical data, clinical data that must be communicated to another patient care team member, and clinical decision support.

Consequences
The therapeutic consequences of clinical surveillance are as follows:

  • Safe patient environment
  • Quality patient care
  • Reassurance to patient-family
  • “Expert,” “proficient” or “novice” nurse ability to adapt to any untoward clinical situation, with

technical continuous surveillance running in background

  • Early intervention critical thinking and action to prevent the escalation of a significant clinical event.

Model case

This step in the concept analysis of clinical surveillance is the construction of a model case that is a pure illustration of the concept’s use, in which all of its critical attributes are included to obtain the best
possible patient outcome (Walker & Avant, 2019). The seven attributes of clinical surveillance include attention, timeliness, recognition, intuition, analysis, action and collaboration (as defined previously).

A model example of clinical-surveillance follows:

Ms. B, a 38-year old active female, avid runner, teacher and mother, presents to the hospital for an anticipated two- to three-day/night stay following a surgical repair of a grade III anterior cruciate ligament (ACL) tear and a spiral shank fracture to the tibia. Significantly, the patient has a medical history of insulin dependent type I diabetes.

Post-surgery, Ms. B has been ventilator-free for the last four hours, now receiving 3/l O2 via nasal canula, integrated continuous non-invasive capnography, continuous pulse-oximetry (SpO2) and non-invasive blood pressure every two hours for the first eight, then every four hours thereafter.

The patient is discharged from the post-anesthesia recovery room and admitted to a medical-surgical unit for post-surgical care. She receives continuous respiratory surveillance while on opioids for post-operative pain; and is monitored for both oxygen saturation (SpO2) along with remote non-invasive capnography.

While both capnography and SpO2 are warranted, capnography provides an immediate indication of apnea, whereas pulse oximetry will show a high saturation for several minutes. Recent studies indicate that the use of capnography in post-operative patients is an early predictor for OIRD (Lam, 2017;  (Voscopoulos et al., 2014; Wong, Mabuyl & Gonzalez, 2013; Garah, Avid, Rosen & Shaoul, 2015; Supe, 2017).

The unit’s nurses sought better, more reliable surveillance tools for many years, as the increase in acuity of patients has permeated general care units. This is not a new problem; instead, it is one that has placed the general floor nurses at a disadvantage, as they experience the demands of caring for sicker patients with less resources, simply by virtue of their unit function. The proper state-of-the-science technology tools must be placed in the nurse’s resource toolbox on the general care unit. The use of capnography is an example of one essential tool.

In addition to the narcotics for pain, Ms. B receives intravenous (IV) therapy and scheduled sub- cutaneous (sub-Q) insulin for her diabetes. The post-op incision to the right knee is minimal, covered by a sterile dressing, and the lower leg is wrapped in ACE bandages, framed in a full-leg steel brace. Ms. B’s primary care nurse has more than 20 years of critical care and medical/surgery unit experience.

The primary night nurse for Ms. B. is about to make her morning rounds at 6 a.m. when her attention shifts to a capnography trending notification. Upon entering the room, the nurse finds Ms. B. lethargic, pale, diaphoretic, with an SpO2 via pulse oximetry of 87%, respiratory rate of 8, an etC02 reading of 48 mm Hg. (normal value is between 35-45 mm Hg.), and serum glucose via finger-stick was 55 mg/dL (normal value 70-120mg/dL).

The timeliness of having system-integrated continuous respiratory clinical surveillance allows the nurse to be notified that the patient is clinically trending toward OIRD. “Continuous capnography monitoring in patients receiving PCA significantly reduces the incidence of OIRD in the setting of rapid response and unplanned transfers to a higher level of care” (Stites, Surprise, McNiel, Northrop & DeRuyter, 2017, p. 1).

Early detection of respiratory depression makes good medical sense, particularly on medical-surgical units where nurse-to-patient ratios are knowingly at higher levels and unmonitored patient events can go unwitnessed (Carlisle, 2014). The nurse recognizes that analgesia, even in a healthy, active individual, can cause life-threatening complications, such as OIRD, and that further action is needed based on the prospective data.

The nurse successfully shakes the patient awake, then systematically calculates the risks to the patient after reviewing all the pertinent clinical data. The nurse calls for clinical assistance and keeps Narcan (Naloxone) nearby in case there is a need to reverse this very early phase of OIRD and its potential debilitating effects (Dahan, Aarts & Smith, 2010).

A nonpharmacological approach appears to work, as the patient is aroused and gradually becomes more coherent. The nurse continues to talk to the patient, encouraging Mrs. B. to take deep breaths. It is
important to note that OIRD is evidenced by a respiratory rate between eight to 10 breaths/minute, SpO2 below 90% and etC02 above 50 mm Hg. Severe respiratory depression occurs when the respiratory rate falls below eight breaths/minute with SpO2 below 85% for at least six minutes (Carlisle, 2014). Trending is paramount; the respiratory rate decreases as the etC02 rises, and the patient suffers apnea; yet, all the while the SpO2 remains stable. Additionally, the nurse’s years of experience and intuition tell her that the patient’s physiological presentation may be exacerbated due to combination of hypoxia, as well as symptoms of hypoglycemia. For this patient, as is typical for anyone with altered pain perception and drowsiness, it is often difficult to recognize her own symptoms of hypoglycemia. Although the patient’s heart rate is at the higher limit of normal at 98 beats per minute (bpm), she complains of feeling anxious and states she can feel her “own heart beating.”

The nurse collaborates with the hospitalist, and an ECG is done as a precautionary measure. The system software will analyze the ECG, determining that the patient is in normal sinus rhythm, and the clinicians will determine if the anxious feelings the patient is experiencing are due to the hypoxia/hypoglycemia combo, ruling out a cardiac causal-relationship.

The nurse takes a three-fold approach: First, the nurse responds by giving a slow IV push of 50% dextrose (25g/50 ml) prefilled syringe to get the patient’s glucose back to a therapeutic range. The patient’s personal therapeutic range is typically between 115-125 mg/dL.

Secondly, the nurse knows that in patients with underlying risk factors such as type I diabetes, the dose of opioids should be carefully titrated (Gupta et al., 2018). Nurse and hospitalist decide to collaborate with the attending physician regarding opioid dosing.

If not for the respiratory continuous clinical surveillance and subsequent notification, the nurse may have found her patient dead in bed. Episodic sets of vital signs represent the patient status in that particular moment. It is a snapshot in time, leaving subsequent time gaps in-between when the patient is unmonitored. It is the consistent, continuous stream of data in the system software that allows for accurate trending of a patient’s vital signs.
Image removed.
The attending physician, hospitalist and primary nurse collaborate via a secure, threaded message on their mobile devices. It is agreed that the patient at this point is markedly improved, now fully awake and responding appropriately with an etC02 of 42 mm Hg., SpO2 of 92%, serum glucose of 120 mg/dL, with stable heart rate and blood pressure. The immediate clinical team decides that it is not necessary to pull in any other ancillary departments at this time. The patient is due for routine lab work in the morning, opioid pain medication will be adjusted appropriately, and continued respiratory surveillance will ensue. The initial 24–48 post-operative hours may denote a particularly sensitive period when patients are predisposed to the development of respiratory insufficiency, as a side effect of opioid administration (Melamed et al., 2016).

All the purest attributes of clinical surveillance (nurse and system) were in place to prevent care escalation, potential patient harm and a probable poor patient outcome. The timeliness of the etC02 notification garnered the full attention of the nurse, allowing her to recognize the seriousness of the matter and take immediate action. The nurse used her intuition to evaluate the entire clinical picture, taking into account both the hypoxia and hypoglycemia aggregate effect. She called upon the hospitalist to collaborate on the patient event and together were able to analyze all of the data points to make an optimal clinical plan of action and care for the patient. The nurse used not only experience but had an optimal system in place, which notified her of adverse trending on a specific life-dependent data point. The outcome is safe quality nursing care. Patient needs drive nurse competencies, and system requirements and resources.

Borderline case

A borderline case provides the same example, which contains most of the defining attributes of the concept of clinical surveillance being examined, but not all of them. For this borderline example, the patient and the case remain the same as the one depicted in the model example, but the attributes of timeliness and intuition will be removed.

The primary night nurse for Mrs. B. is making her 6 a.m. rounds. When she walked into the room to take the patient’s vital signs, she noted that a red alarm was sounding, as she turned her full attention to the
patient, whom she found to be semi-conscious, pale, diaphoretic, with an SpO2 of 85%, respiratory rate of 8, an etC02 reading of 54 mm Hg. (normal value is between 35-45 mm Hg.), and serum glucose via finger-stick was 55 mg/dL (normal value 70-120mg/dL). With an older model stand-alone device, the nurse was not notified that the etC02 was trending in the wrong direction. Instead, she walked into the room just as a high-level alarm was annunciating.

“Early detection promotes timely rescue, particularly on med/surg units where nurse-to-patient ratios are higher and critical events are less likely to be witnessed” (Carlisle, 2014; Stites et al., 2017). The nurse recognizes that opioid analgesia, even in a healthy, active individual can cause life-threatening complications referred to as OIRD, and that further action is needed based on the adverse data points in this presented in this moment.

The nurse attempts to shake the patient awake, to no avail. The nurse systematically calculates the risks to the patient after reviewing all the pertinent clinical data.

The nurse calls for clinical assistance. A nonpharmacological approach is not an option. The nurse begins to slowly push Narcan (Naloxone) to reverse symptoms of respiratory depression and its potential debilitating effect at a dilution of 0.2 mg naloxone in 10 mL normal saline solution and titrated the dose 1 to 2 mL at a time until symptoms are reversed. Narcan is fairly short acting, so the nurse realizes the patient may need multiple doses. Slowly, the patient becomes more coherent.

The nurse continues to talk to the patient, encouraging her to take deep breaths. It is important to note that OIRD is evidenced by a respiratory rate between eight to 10 breaths/minute, SpO2 below 90%, and etC02 above 50 mm Hg. Severe respiratory depression occurs when the respiratory rate falls below eight breaths/minute with SpO2 below 85% for at least six minutes (Carlisle, 2014). The optimal scenario would have required trending data showing that the respiratory rate decreases as the etC02 rises, and the patient suffers apnea; yet, all the while the SpO2 remains stable. Another aspect of the patient’s presentation that went unnoticed was her hypoglycemic state. The nurse was not intuitive enough to take into account that the patient’s physiological presentation may be exacerbated due to combination of hypoxia as well as symptoms of hypoglycemia. With the respiratory depression and subsequent level of unconsciousness, it would be impossible for the patient to realize her own hypoglycemic state.

The nurse collaborates with the hospitalist and escalation to the ICU is planned. STAT blood gases, labs and ECG are done. The nurse and hospitalist analyze the various results, as they wait for an ICU bed to become available for their patient.

Upon physician order, the nurse gives a slow IV push of 50% dextrose (25g/50 ml) prefilled syringe to get the patient’s glucose back to a therapeutic range. The patient’s personal therapeutic range is typically between 115-125 mg/dL. Secondly, the physician knows that in patients with underlying risk factors such as type I diabetes, the dose of opioids should be carefully titrated (Gupta et al., 2018) and is frustrated that their facility does not have this advanced capability.

Nurse and hospitalist collaborate with the attending physician regarding opioid dosing. In the ICU, although no system integration for data point trending is available, they realize that at least the nurse- patient ratio will be tighter than on the med/surg floor.

The attending physician, hospitalist, primary nurse, respiratory, pharmacy and ICU supervisor collaborate via a secure, threaded message on their mobile devices. The patient at this point is conscious, but lethargic, with an etC02 of 48 mm Hg., SpO2 of 92%, serum glucose of 120 mg/dL, with stable heart rate and blood pressure. The immediate clinical team transfers the patient to the ICU where she will have better observation. Transfer to the ICU from a med/surg unit occurs within 48 hours of surgery in 73% of the cases. Of these cases, approximately 31% require non-invasive ventilation, and 18% require mechanical ventilation (Melamed et al., 2016).

Only some of the attributes of clinical surveillance (nurse and system) were in place. Yet, it was not enough to prevent care escalation. Those patients receiving intravenous opioids during the first 24 postoperative hours were hospitalized four days longer, had higher in-hospital mortality, and had excess hospitalization costs of at least US$26,571 (Melamed et al., 2016).

Furthermore, estimated additional costs related to post-operative pulmonary complications (PPC) range from $5,983 to $120,579 per event, with higher costs associated with mechanical ventilation or tracheostomy (Sabate, Mazo & Canet, 2014). The harm to the patient, and subsequent financial implications, could have easily been avoided if the nurse was prospectively notified of the adverse etC02 trending prior to the life-threatening alarm. Retrospective data prevents the nurse from being proactive. Alternatively, data after the fact hastens a reactionary response.

In this case, system tools were not in place to prevent patient harm. The untimeliness of the etC02 alarm did capture the full attention of the nurse, and she was able to recognize the seriousness of the matter and take immediate action. The issue here is that her response to the situation was reactionary. The nurse was not intuitive enough to evaluate the entire clinical picture that would take into account both the hypoxia and hypoglycemia aggregate effect. She did have the foresight to call for help and called upon the hospitalist to collaborate on the patient event.

Following the delivery of Narcan and glucose, they were able to analyze all of the data points to make a clinical plan of action and care for the patient. The nurse has used what experience she had to notify the hospitalist of the poor patient situation, while applying her inherent skill set to evaluate the life- dependent retrospective data points to develop an immediate care plan under the circumstances. The outcome was sub-optimal nursing care. The system failed the nurse. Without the timeliness of proactive clinical intervention that a surveillance system would broadcast, the patient was placed in harm’s way. In addition, the nurse lacked the intuitiveness to acknowledge the hypoglycemic event within the overall patient presentation. This is clearly an event that would otherwise be easily preventable. Patient needs drive nurse competencies, and system requirements and resources. Here, the nurse was not given the proper tools to promote safe, effective nursing care.

Related case

In this case, there are related instances of the concept being studied. Some of the defining attributes are missing that are different attributes missing from the borderline case above. In this example, the patient
and case are the same as the previous two models. The attributes of timeliness and intuition are there, but the attributes of attention, recognition and analysis are absent.

The primary night nurse for Ms. B. is about to make her rounds at 6 a.m. when her nurse colleague told her that she did not answer the capnography alert for her patient (delivered to her mobile device), so it was automatically escalated to him.

Both nurses found Ms. B. to be extremely lethargic, pale, diaphoretic, with an SpO2 of 87%, respiratory rate of 8, etC02 reading of 48 mm Hg. (normal value is between 35-45 mm Hg.), and serum glucose via finger-stick was 55 mg/dL (normal value 70-120mg/dL).

The timeliness of having system integrated continuous respiratory clinical surveillance allowed the nurse to be notified that the patient was clinically trending in an adverse direction. “Continuous capnography monitoring in patients receiving PCA significantly reduces the incidence of OIRD in the setting of rapid response and unplanned transfers to a higher level of care” (Stites et al., 2017, p. 1). Early detection of respiratory depression makes good medical sense, particularly on medical-surgical units where nurse-to- patient ratios are knowingly at higher levels and unmonitored patient events can go unwitnessed.

The nurse is unaware that opioid analgesia, even in a healthy, active individual, can cause life-threatening complications referred to as OIRD. The second nurse recognizes that further action is needed based on the prospective data. The second nurse tells the primary nurse to call the hospitalist, as he begins to successfully rouse the patient awake. As the hospitalist arrives at the bedside, the second nurse resumes his respective routine responsibilities. The primary nurse knew that some sort of action must quickly ensue, so decides to follow directions from the hospitalist. She is fully able to collaborate as directed with the intent to minimize the patient risk for poor outcome.

The nurse watches as the hospitalist takes a nonpharmacological approach to the situation. He has Narcan (Naloxone) nearby in case there is a need to reverse this very early phase of OIRD and its potential debilitating effect. A nonpharmacological approach appears to be working, as the patient is arousable and
gradually becoming more coherent. The hospitalist and primary nurse continue to talk to the patient, encouraging her to take deep breaths. It is important to note that OIRD is evidenced by a respiratory rate between eight to 10 breaths/minute, SpO2 below 90%, and etC02 above 50 mm Hg. Severe respiratory depression occurs when the respiratory rate falls below eight breaths/minute with SpO2 below 85% for at least six minutes (Carlisle, 2014).

Trending is paramount; the respiratory rate decreases as the etC02 rises, and the patient suffers apnea; yet, all the while the SpO2 remains stable. Being a novice nurse with only one month of experience, she lacked the intuition to tell her that the patient’s physiological presentation was exacerbated due to the combination of hypoxia as well as symptoms of hypoglycemia.

For this patient, as is typical for anyone with altered pain perception and drowsiness, it was most likely difficult to recognize her own symptoms of hypoglycemia. The primary nurse continues to follow the direction of the hospitalist. Although the patient’s heart rate is at the higher limit of normal at 98 beats per minute (bpm), she is complaining of feeling “anxious” and states she can feel her “own heart beating”. The nurse collaborates with the hospitalist and an ECG is done as a precautionary measure. The hospitalist and nurse analyze the ECG, determining that the patient is in normal sinus rhythm. The anxious feelings the patient is experiencing are a direct consequence of the hypoxia/hypoglycemia combination, ruling out a cardiac causal-relationship.

The novice nurse takes a unifocal approach, calling for help. She watches as the hospitalist gives a slow IV push of 50% dextrose (25g/50 ml) prefilled syringe to get the patient’s glucose back to a therapeutic range. The patient’s personal therapeutic range is typically between 115-125 mg/dL. Secondly, most importantly, the primary nurse was unaware that respiratory continuous clinical surveillance and subsequent notification saved the patient’s life.

The attending physician, hospitalist and primary nurse collaborate via a secure, threaded message on their mobile devices. It is agreed that the patient at this point is markedly improved, now fully awake and responding appropriately with an etC02 of 42 mm Hg., SpO2 of 92%, serum glucose of 120 mg/dL, with stable heart rate and blood pressure. The immediate clinical team decides that it is not necessary to pull in
any other ancillary departments at this time. The patient is due for routine lab work in the morning, opioid pain medication will be adjusted appropriately, and continued respiratory surveillance will be continued. The initial 24–48 post-operative hours may denote a particularly sensitive period when patients are predisposed to the development of respiratory insufficiency, as a side effect of opioid administration (Melamed et al., 2016).

Only some of the attributes of clinical surveillance (nurse and system) were in place to prevent care escalation, potential patient harm, and a probable poor patient outcome. Important to note in this particular case is the timeliness of the etC02 notification. Although the alert was escalated to her colleague, she quickly learned the seriousness of the matter, which prompted her to take immediate action and seek help. The primary nurse lacked the years of experience and intuitiveness to evaluate the entire clinical picture, taking into account both the hypoxia and hypoglycemia aggregate effect. She, however, felt compelled to seek help and was able to collaborate with the hospitalist on the patient event. It was the hospitalists who analyzed all of the data points to make an optimal clinical plan of action and care for the patient. Nurse A, although novice, was able to partake in the scenario that saved the patient’s life because there was timely notification of adverse trending of etC02 via continuous clinical surveillance. Although the nurse lacked some critical attributes, the clinical surveillance system was in place to promote a favorable patient outcome. Patient needs drive nurse competencies, and system requirements and resources.

Contrary case

A contrary case is a clear example of what the concept is not. None of the defining attributes are present in this case example. The following contrary model retains the patient and case but includes none of the defining attributes of clinical surveillance.

The primary night nurse for Ms. B. makes her 6 a.m. rounds. When she walks into the room to take the patient’s vital signs, she gently taps her patient to awake her. When she can’t stir the patient awake, she checks the patient’s pulse. The pulse is absent. The primary nurse calls a code-blue and starts cardiopulmonary resuscitation, to no avail. The code team arrives and takes over but cannot revive her.

None of the attributes of clinical surveillance (nurse and system) were in place. Attention, timeliness, recognition, action, intuitiveness, collaboration and analysis were absent. The nurse used what little experience she had to call a code. From that point forward, she was an observer.

The consequence was ultimately the poorest possible outcome. Both nurse and system failed. Without the timeliness of proactive clinical intervention that a surveillance system would broadcast, the patient was placed in harm’s way. In addition, the nurse lacked the other attributes that may have given her time- management and intuition. This is a case which could easily have been prevented. Patient needs drive nurse competencies, and system requirements and resources. Here, the novice nurse lacked guided preceptorship. Deficient, too, were the proper surveillance tools for the novice nurse to promote safe, effective nursing care.

Empirical referents

The final step in the concept analysis method is to define empirical referents of the concept. This last step takes the abstract “attributes” and translates them into measurable outcomes. Empirical referents "are classes or categories of actual phenomena that by their existence or presence demonstrate the occurrence of the concept itself" (Walker & Avant, 2019, p. 34).

  • Technology presence of one or more of the following: preemptive clinical analytics tools, physiological monitors, ventilators, electronic health record.
  • Communication: Verbal; i.e., between nurse-patient-family-clinical team.
  • Communication: Technological; i.e. notification, alert, alarm, mobile phones.
  • Collaboration: between nurse-nurse/nurse-doctor/nurse-care team: verbal, text, written, telephone.
  • Patient care: ongoing processes/outcomes/actions as result of nurse decision-making.
  • Clinical decision support: i.e. patient symptoms, analytic software, middleware, rules-based-notification, physiological monitors, lab reports, ECG, X-ray, pharmacy, and care team
    collaboration.

Assumptions

Assumptions revolve around a therapeutic relationship in the nurse-patient care model that may influence surveillance:

  • Surveillance (by nurse) will increase the quality of patient-care.
  • Surveillance (by nurse) will increase patient safety.
  • Communication will facilitate care team coordination and collaboration, streamline processes, and help the family members to feel more secure.
  • Integration of advanced system modalities, such as preemptive clinical analytics, provide continuous analysis of the patient condition: 1) singular trending of individual vital signs - algorithm anomaly prior to physiological alarms and clinical alerts; and 2) multivariate trending clinical decision support - combining more than one event that is conditionally based on continuously aggregated data to determine whether a patient is actually in distress (i.e. respiration and heart rate displayed together as part of a richer profile of a patient’s current condition) with the intent to notify the nurse of potential problems before they occur - allowing a subsequent escalation pathway to the nucleus patient care team.

Sum: The ideal therapeutic nurse-patient relationship via the various attributes of surveillance protects the acutely ill patient from debilitating life-threatening complications while providing support for the family and collaboration between nurse and essential patient care team. In addition, this model provides the nurse-patient care cycle with exemplar quality nursing characteristics that meets patient needs (as founded in the synergy model), therefore improving patient outcomes.

Additional clinical framework use-models
While this particular piece of work is a review of “nurse and system,” the same concept may be applied to other disciplines and/or use-models. For example; the recent Spyglass Report illustrated the use of clinical surveillance from a purely technical framework of interoperability, where it is perhaps better globally understood (Malkary, 2018).

It is of tantamount importance to examine the fundamental attributes of clinical surveillance origins, however, which is the nurse who is at the epicenter of all patient care. The nurse-patient relationship is the bedrock, nuclear foundation from which all other clinical surveillance models promulgate. Bringing real- time clinical surveillance and analytics solutions into the original concept, nurses can now watch trends to identify the early onset of patient deterioration or life-threatening events requiring care team intervention. It is the data trending and analysis combined with clinical expertise that can be used to detect serious patient deterioration. One attribute drives the other.

Other frameworks of reference may be physician-patient-technology, clinical team communication and technology, and hospital-homecare-technology. There are many perspectives from which the attributes of attention, timeliness, recognition, intuition, analysis, action and collaboration may be applied.

Conclusion

Clinical surveillance is the quintessential basis of good nursing practice. Although we are beginning to conceptualize the professional meaning of clinical surveillance, a strong need to operationalize the technical aspect of surveillance into our nursing patient-safety standards and patient-safety outcomes still exists. With today’s advanced clinical technology component (case in point being the inception of the unified, preemptive analytics tools in the acute patient-care model), an understanding of clinical surveillance at a conceptual level is evermore imperative today to patient safety in an era of ubiquitous technology.

References

Alex, D. (2018). AeroVironment RQ-11 Raven. Small Unmanned Aerial Vehicle (SUAV). https://www.militaryfactory.com/aircraft/detail.asp?aircraft_id=888. Retrieved from www.militaryfactory.com

Benner, P. (1984). From Novice to Expert: Excellence and power in clinical nursing practice. Menlow Park, CA: Addison-Wesley Publishing Company.

Butcher, H., Bulechek, G., Dochterman, J., & Wagner, C. (2018). Nursing Interventions Classification (NIC) (7th ed.): Elsevier Health Science.

Carlisle, H. (2014). The case for capnography in patients receiving opioids. American Nurse Today, 9(9), 22-27.

CDC (2018). Public health surveillance and data.  https://www.cdc.gov/surveillance/index.html.

CMS (2014). New CMS guidance recommends monitoring of all patients receiving opioids: what it means for healthcare providers. (Clinical Leadership & Infection Control). Becker’s Hospital Review Retrieved from https://www.beckershospitalreview.com/quality/new-cms-guidance-recommen…

Dahan, A., Aarts, L, & Smith, T. (2010). Incidence, reversal, and prevention of opioid-induced respiratory depression. Anesthesiology, 112(1), 226-238.

DuPree, E. (2016). High reliability: the path to zero harm. Healthcare Executive (Jan/Feb), 66-69. http://www.jcrinc.com/assets/1/7/PathToZeroHarm.pdf

Ferguson, A. (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, New York University Press.

Garah, J., Avid, O.E., Rosen, I., & Shaoul, S. (2015). The Value of Integrated Pulmonary Index (IPI), monitoring during endoscopies in children. Journal of Clinical Monitoring and Computing, 29(6), 773-778.

Giuliano, K. (2017). Improving patient safety through the use of nursing surveillance. AAMI Horizons (Spring), 34-43.

Gupta, K., Prasad, A., Nagappa, M., Wong, J., Abrahamyan, L., & Chung, F. (2018). Risk factors for opioid-induced respiratory depression and failure to rescue: a review. Current Opinion in Anesthesiology, 31(1), 110-119.

Hardin, S., & Kaplow, R. (2017). Synergy for Clinical Excellence: The AACN Synergy Model for Patient Care. (2nd ed.). Burlington, MA: Jones & Bartlett Learning

Heslop, L., & Lu, S. (2014). Nursing‐sensitive indicators: a concept analysis. Journal of Advanced Nursing, 70(11): 2469–2482.

Kelly, L., & Vincent, D. (2011). The dimensions of nursing surveillance: a concept analysis. Journal of Advanced Nursing, 67(3), 652-661.

Lam, T. (2017). Continuous Pulse Oximetry and Capnography Monitoring for Postoperative Respiratory Depression and Adverse Events: A Systematic Review and Meta-analysis, Anesthesia and Analgesia, 125(6):2019-2029.

Malkary, G. (2018). Healthcare without bounds Trends in clinical surveillance and analytics. Menlo Park, CA: Spyglass Consulting Group.

Marshall, J.C., Bosco, L, Adhikari, N.K., Connolly, B., Diaz, J.V., Dorman, T., . . . Zimmerman, J. (2017). What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine. Journal of Critical Care, 37, 270- 276.

Melamed, R., Boland, L.L., Normington, J.P., Prenevost, R.M., Hur, L.Y., Maynard, L.F., . . . Huguelet, J. (2016). Postoperative respiratory failure necessitating transfer to the intensive care unit in orthopedic surgery patients: risk factors, costs, and outcomes. Perioperative Medicine, 5(19). Retrieved from https://perioperativemedicinejournal.biomedcentral.com/track/pdf/10.1186/s13741-016-0044-1

Merriam-Webster. (Retrieved 2018, June 21). Definition of surveillance. Retrieved from
http://www.m-w.com

Milhomme, D., Gagnon, J., & Lechasseur, K. (2018). The clinical surveillance process as carried out by expert nurses in a critical care context: A theoretical explanation. Intensive and Critical Care Nursing, 44, 24–30.

Mohammadipour, F., Atashzadeh-Shoorideh, F., Parvizy, S., & Hosseini, M. (2017). Concept development of “Nursing Presence”: Application of Schwartz-Barcott and Kim's Hybrid Model. Asian Nursing Research, 1(11), 19-29.

Pfrimmer, D.M., Johnson, M.R., Guthmiller, M.L., Lehman, J.L., Ernste, V.K., & Rhudy, L. (2017). Surveillance: A nursing intervention for improving patient safety in critical care environment. Dimensions of Critical Care Nursing, 36(1), 45-52.

Piehler, G.K., & Johnson, M. (2013). Encyclopedia of Military Science: Sage Publications.

Sabate, S., Mazo, V., & Canet, J. (2014). Predicting postoperative pulmonary complications:
implications for outcomes and costs. Current Opinions in Anaesthesiology, 27, 201-209.

Stites, M., Surprise, J., McNiel, J., Northrop, D., & DeRuyter, M. (2017). Continuous Capnography reduces the incidence of opioid-induced respiratory rescue by hospital rapid
resuscitation team. Journal of Patient Safety. https://doi-org.ezproxy.kpu.ca:2443/10.1097/PTS.0000000000000408

Supe, D. (2017). Continuous surveillance of sleep apnea patients in a medical-surgical unit
Biomedical Instrumentation & Technology, May/June, 236-251.

The Joint Commission. (2017). Joint Commission Enhances Pain Assessment and Management Requirements for Accredited Hospitals. The Joint Commission Perspectives, 37(7). Retrieved from https://www.jointcommission.org/assets/1/18/Joint_Commission_Enhances_Pain_Assessment_and_Management_Requirements_for_Accredited_Hospitals1.PDF

Thesaurus online. (n.d.) https://www.thesaurus.com

Voscopoulos, C.J., MacNabb, C.M., Freeman, J., Galvagno, S.M., Ladd, D., George, E. (2014). Continuous noninvasive respiratory volume monitoring for the identification of patients at risk for opioid-induced respiratory depression and obstructive breathing patterns. Journal of Trauma and Acute Care Surgery, 77(3), S208-S215. doi:10.1097/TA.0000000000000400
26

Walker, L., & Avant, K. (2019). Strategies for Theory Construction in Nursing., (6th ed.). Toronto: Pearson.

Watts, A.C., Ambrosia, V.G., & Hinkley, E. (2012). Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sensing, 4, 1671- 1692.

Welch, J. (2015). Surveillance monitoring: an emerging standard of care. 24 X 7: Solutions for Healthcare Technology Management. Retrieved from http://www.24x7mag.com/2015/09/surveillance-monitoring-emerging-standard-care/

WHO (2018). Types of Surveillance. Immunization, Vaccines and Biologicals.
http://www.who.int/immunization/monitoring_surveillance/burden/vpd/surv… /en/.

Wong, M. (2014). What does new CMS guidance on monitoring post-operative patients receiving opioids mean for hospital practice? Patient Safety and Quality Healthcare (PSQH). https://www.psqh.com/news/what-does-new-cms-guidance-on-monitoring-post-operative-patients-receiving-opioids-mean-for-hospital-practice/

Wong, M., Mabuyl, A., & Gonzalez, B. (2013). First National Survey of Patient-Controlled Analgesia Practices. Retrieved from Physician-Patient Alliance for Health & Safety
www.premiersafetyinstitute.org/wp-content/uploads/PPAHS-national-survey…- analgesia.pdf

Author Bio:

Dr. Mary Jahrsdoerfer is a clinical authority in emerging healthcare technology, bridging the gap to safe clinical practice and patient safety in the acute care setting. Mary currently serves as Chief Nursing Information Officer at Bernoulli Healthcare, with a focus of improving patient safety in real time through clinical analytics. In addition, Dr. Jahrsdoerfer is the director of graduate studies, healthcare informatics, and clinical assistant professor at Adelphi University in New York. Prior, she held leadership roles including the Chief Nursing Officer for Extension Healthcare (now Vocera Communications) and clinical research scientist and clinical consultant at Philips Healthcare. Mary has her clinical foundation as a critical care nurse, with a focus in cardiology. Mary has many years of experience on the front lines of nursing, hospital leadership, and complex program development, at various healthcare systems in the northeastern United States. Her career interests grew to emerging healthcare technologies specific to patient safety

Posted on