Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Medication discrepancies across multiple care transitions: A retrospective longitudinal cohort study in Italy

  • Marco Bonaudo ,

    Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

    marco.bonaudo@ unito.it

    Affiliation Department of Public Health Sciences and Pediatrics, Università di Torino, Torino, Italy

  • Maria Martorana,

    Roles Data curation, Formal analysis, Investigation, Writing – original draft

    Affiliation Department of Public Health Sciences and Pediatrics, Università di Torino, Torino, Italy

  • Valerio Dimonte,

    Roles Validation

    Affiliation Department of Public Health Sciences and Pediatrics, Università di Torino, Torino, Italy

  • Alessandra D’Alfonso,

    Roles Supervision, Validation

    Affiliation Public Health Authority, ASLTO2, Torino, Italy

  • Giulio Fornero,

    Roles Conceptualization, Methodology

    Affiliation AOU Città della salute e della Scienza, Teaching Hospital, Torino, Italy

  • Gianfranco Politano,

    Roles Formal analysis, Methodology, Validation, Writing – original draft

    Affiliation Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy

  • Maria Michela Gianino

    Roles Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Public Health Sciences and Pediatrics, Università di Torino, Torino, Italy

Medication discrepancies across multiple care transitions: A retrospective longitudinal cohort study in Italy

  • Marco Bonaudo, 
  • Maria Martorana, 
  • Valerio Dimonte, 
  • Alessandra D’Alfonso, 
  • Giulio Fornero, 
  • Gianfranco Politano, 
  • Maria Michela Gianino
PLOS
x

Abstract

Purpose

Medication discrepancies are defined as unexplained differences among regimens across different sites of care. The problem of medication discrepancies that occur during the entire care pathway from hospital admission to a local care setting discharge (namely all types of settings dedicated to formal care other than hospitals) has received little attention in the medical literature.

The present study aims to (1) determine the prevalence of medication discrepancies that occur during the entire care pathway from hospital admission to local care setting discharge, (2) describe the discrepancy and medication type, and (3) identify potential risk factors for experiencing medication discrepancies in patient care transitions. Evidence from an integrated health care system, such as the Italian one, may explain results from other studies in different healthcare systems.

Methods

A retrospective longitudinal cohort study of patients admitted from July 2015 to July 2016 to the Giovanni Bosco Hospital serving Turin, Italy and its surrounding territory was performed. Discrepancies were recorded at the following four care transitions: T1: Hospital admission; T2: Hospital discharge; T3: Admission into local care settings; T4: Discharge from local care settings. All evaluations were based on documented regimens and were performed by a team (doctor, nurse and pharmacists).

Results

Of 366 included patients, 25.68% had at least one discrepancy. The most frequent type of discrepancy was from medication omission (N = 74; 71.15%). Only discharge from a long-stay care setting (T4) was significantly associated with the onset of discrepancies (p = 0.045). When considering a lack of adequate documentation, not as missing data but as a discrepancy, 43.72% of patients had at least one discrepancy.

Conclusions

This study suggests that an integrated health care system, such as Italian system, may influence the prevalence of discrepancies, thus highlighting the need for structured multidisciplinary and, if possible, computerized medication reconciliation to prevent medication discrepancies and improve the quality of medical documentation.

Introduction

Medication discrepancies are defined as unexplained differences among regimens across different sites of care [1]. They are categorized into documented and undocumented discrepancies, the latter of which are further subdivided into intentional or unintentional.

Recent scientific evidence considers medication discrepancies to be an important public health problem with clinical and economic consequences [24]. Indeed, discrepancies can be harmful, leading to inadequate prescriptions, interruptions of treatment, adverse drug events, longer hospital stays and an increase in hospital readmissions and emergency room visits, which are expensive for the health care system [27]. Several studies have identified care transitions as time points with a greater risk of drug discrepancies [810]. Additionally, medication discrepancies in care transitions could occur more easily in the absence of a formalized system for medication reconciliation and with poor quality of communication between primary and secondary care [2].

Medication reconciliation is a formal process that involves matching the medicines that the patient should be prescribed with those that are actually prescribed and involves adequately reporting any therapy change [3,11]. Medication reconciliation has been identified in the literature as an important tool for preventing pharmacological discrepancies and the World Health Organization, in the “Action on Patient Safety”, considers medication reconciliation to be one of the five top strategies for ensuring patient safety [12]. Additionally, the Joint Commission on Accreditation of Healthcare Organizations included medication reconciliation in the accreditation standards [13]. The problem of medication discrepancies that occur during the entire care pathway from hospital admission to a local care setting discharge (namely, all types of settings dedicated to formal care other than hospitals) has received little attention in the medical literature. Indeed, there are studies in the literature on single points of transitions of care: most analyze discrepancies upon hospital admission or discharge [3,4,1418], and little is known about the prevalence of medication discrepancies upon admission or discharge from local care settings, such as the home and rehabilitation facilities [1,19]. According to the literature unintended medication discrepancies occurred in 25%-70% of hospital admissions and in 33%-96% of hospital discharges [4,6,2022] and up to 71% of patients admitted to local care settings [1,19].

Current evidence is limited in Italy, and there are no studies conducted in a local care setting [11].

New evidence may be important from an integrated health care system, such as the Italian health system, and may help to explain results from other studies in other countries with different healthcare systems. Italy’s healthcare system is a regionally organized national health service (Servizio Sanitario Nazionale, SSN) that has two underlying principles. First, every Italian citizen and foreign resident has the right to healthcare, and second, the system covers all necessary treatments. Local health authorities (ASL) are responsible for the management of all health services in their area, from prevention to care in hospitals and in any type of setting dedicated to formal care other than hospital care and located in the districts. Italy’s healthcare system has made considerable efforts to achieve co-ordination and integration of care, which allows the patient to be followed throughout his/her care pathway. For this aim, one unit in hospitals (NOCC) and one in the district (NDCC) that oversee all transitions of care to manage the most complex medical cases and to ensure the admission to and discharge from a health-care setting were introduced. With Recommendation 17 [23], the Ministry expects that during this pathway attention will also be devoted to pharmacological reconciliation.

The present study aims to (1) determine the prevalence of medication discrepancies that occur during the entire care pathway from hospital admission to a local care setting discharge, (2) describe the type of discrepancy and type of medication, and (3) identify potential risk factors for experiencing medication discrepancies during patient care transitions.

Methods

A retrospective longitudinal cohort study of patients admitted to the Giovanni Bosco Hospital which serves Turin, Italy and its surrounding territory was performed. The following four local care settings: (i) Long-stay Care, (ii) Rehabilitation, (iii) Supported discharge in multiple facilities and (iv) Integrated Home Care (ADI), were studied. Giovanni Bosco Hospital is a hub urban hospital in Turin in Local Health Authority ASL TO2, with more than 3400 health workers and approximately 330 beds and 11,000 discharges/year. It covers an area of over 420,000 inhabitants. Local care setting were positioned in Turin city and on its surrounding territory [24,25]. The study included all patients admitted to the Giovanni Bosco Hospital from July 2015 to July 2016 who met the following inclusion criteria: (i) age > 18 years, (ii) discharged from one of the four hospital wards (internal medicine, geriatrics, neurology, and orthopedics), (iii) admitted to and discharged from one of the four local care settings indicated above, and (iv) beginning and closing their clinical pathway during the above-cited period.

Discrepancies, defined as any undocumented and unintentional incongruity between the documented sources that could result in therapy errors [11,14], were recorded for the following four care transitions: T1: hospital admission; T2: hospital discharge; T3: admission to local care setting; T4: discharge from local care setting. Medication discrepancies were identified using a structured tool that retraces the medication reconciliation process and uses the same variables and sources of documented regimens defined by Ministero della Salute Recommendation [23]. Supported by the above cited recommendation, this analysis was conducted by a team of all professional categories involved in medication treatment (a medical doctor, a nurse and two clinical pharmacists, able to evaluate if the decided treatments were according to the patient clinical pathway) in the following way. For T1, the emergency department (ED) list recorded on the ED form was compared to the first prescriptions in the hospital ward that were recorded in the chart. For T2, the prescriptions on the last hospitalization day recorded on the chart were compared to the therapy list in the discharge letter. For T3, the prescriptions in the discharge letter were compared to the first prescriptions in the local care setting that were recorded on the chart. For T4, the prescription from the last care day were compared to the prescriptions recorded in the discharge letter. Evaluation of medication discrepancies was performed by the team. The doctor and the nurse, separately, analyzed transitional documentation and reported all discrepancies detected, and all the discrepancies were subjected to the pharmacists’ evaluation. Then, by re-evaluating the patients’ clinical history, the team defined whether discrepancies were in line with the care pathway (intentional) or whether they were the result of a clinical error (unintentional). In more complicated cases the doctor who had treated the patient was consulted. Only, the unintentional discrepancies were recorded. Discrepancies were classified as omissions of medication; errors/omissions of the drug name, dose, dosage, frequency and administration type; duplications; and interactions. The drugs involved in the discrepancies were categorized by their mechanisms of action. This study termed “not evaluable discrepancies” as the following: i) the patient was admitted to the hospital ward immediately without transitioning to the ED; and ii) there was no discharge letter because the patient died or was transferred to the ED from a local care setting. The “not evaluable discrepancies” were always considered to be missing.

This study defined “lack of documentation” as any situation in which there was no trace of a clinical document. As the “lack of a documentation” did not allow to assess the presence or absence of discrepancies, we evaluated two possible hypotheses. In the first hypothesis, assuming that “lack of documentation” was a “not evaluable discrepancies” was considered to be missing; in the second hypothesis, “lack of documentation” was considered to be a discrepancy with the aim of considering the worst possible scenario.

Statistical analysis

To automate statistical analysis, we developed a general and robust R [26] pipeline that processed the input data through the following steps:

Data sources.

The input data were presented to the pipeline as a csv file in which each row represented a patient along with his or her drug discrepancies. Given this format, multiple rows belonging to the same patient were allowed (e.g., a single patient might be discrepant for more than one drug).

Data matrix columns contained several attributes used for stratification: personal attributes (e.g., gender, age, nationality) and discrepancy attributes (e.g., missing or erroneously administered drugs) detailed for each transition of care. Moreover, during the preprocessing phase, raw attributes have been combined into functions that allowed to compute derived stratification values (e.g., the difference between hospital admission and discharge dates has been used to compute the number of days a person has been hospitalized), which in turns resulted into more meaningful data for further analysis steps.

Descriptive analysis.

Preprocessed data have been analyzed in terms of cumulative analysis and descriptive analysis. In the cumulative analysis we computed the global discrepancy assessment, by taking into account multiple discrepancies (when available) for every single patient, which resulted in 398 rows, one for every combination of a patient and a specific discrepancy. In the latter analysis, we instead resorted to a patient-centric analysis. We therefore merged multiple discrepancies of a single patient in a single row, thus reducing the database to 366 rows, one for each patient.

The pipeline produced as output two files as follows: i) cumulative analysis, and, ii) descriptive analysis. The first file, cumulative analysis, contained for each transition of care the stratification in terms of categories. The figures in the output file were expressed as cumulative value and relative percentage over the totals. In the descriptive analysis output file, all the database columns of interest were analyzed according to their nature. Categorical variables have been stratified, while numerical variables have been analyzed in terms of statistical distribution computing their mean, standard deviation and quartile distribution. Some numerical variables that allowed for further categorization (e.g., age) have also been factorized into groups (i.e., age groups) in order to simplify the following odds ratio analysis. Moreover, patients with “lack of documentation” have been analyzed in two different hypotheses depicted above. To obtain more consistent and conservative measures "not evaluable discrepancies" patients have been programmatically removed.

Stratification & risk analysis.

After the descriptive analysis, the pipeline automatically computed a confusion matrix for each categorical variable in order to perform significance tests, and data were prepared in a convenient fashion for further odds ratio analysis. Confusion matrices were then corrected by evaluating numerosity constraints and discarding low abundance strata (fewer than 8 elements) in order to reach sufficient significance in the statistical tests. Both the Chi-Square test (high numerosity strata) and Fisher exact test (low numerosity strata) were performed. For computing the drug discrepancy odds ratio for each stratum, we defined a control value to be used as a reference for each confusion matrix, and we used the strata with the lowest marginal probability as a control (i.e., with odds ratio = 1) and mapped the other strata against this one, guaranteeing that all of the other odds ratios were greater than 1. An output file summarizes every stratification in terms of its odds ratio along with 95% confidence intervals and the p-value of both the chi-square and Fisher’s exact tests.

The study was approved by Ethics Committee of the Turin territory, Prot. N. 0012774, August 1, 2016. An informed written consent was obtained in agreement with Authorization on processing personal data GU n. 302, December 27, 2013. The patients’ private health information (PHI) was ensured by the de-identification of data.

Results

Between July 2015 and July 2016, 366 patients met the inclusion criteria and were included in the study. The mean (SD) age of the patients was 80.77 (10.66) years, and 57.92% were female. Table 1 lists the patient characteristics.

Most (93.72%, N = 343) of the enrolled patients were from the emergency department, while 6.28% (N = 23) had a planned hospital admission.

Of the participants, 43.44% were admitted to internal medicine, 27.05% to orthopedics, 18.58% to geriatrics, 8.74% to neurology, 1.09% to surgery, 0.55% to cardiology and 0.55% to the intensive care unit. The patients’ hospital discharge rates were 42.35% from internal medicine, 27.32% from orthopedics, 19.67% from geriatrics and 10.66% from neurology. Additionally, 36.06% of patients were admitted to rehabilitation settings, 34.43% to supported discharge settings, 24.04% to long-stay care and 5.46% to integrated home care (ADI) (Fig 1).

thumbnail
Fig 1. Number of patients sampled in the care pathway through health facilities.

T1: Hospital admission T2: Hospital discharge T3: Admission into local care setting T4: Discharge from local care setting.

https://doi.org/10.1371/journal.pone.0191028.g001

First hypothesis: “lack of documentation” was considered to be missing

Considering the first hypothesis, the total number of discrepancies was 104; pharmacological discrepancies involved 94 patients, so 25.68% of the total number of patients had at least one discrepancy. The most frequent type of discrepancy was omission of medication (N = 74; 71.15%) (see Table 2).

Furthermore, the most frequent category of drugs involved in the discrepancy was “proton pump inhibitors” (N = 11; 11.34%) (see Table 3).

thumbnail
Table 3. Drug categories involved in at least one discrepancy, first hypothesis (N = frequencies).

https://doi.org/10.1371/journal.pone.0191028.t003

Table 4 shows the association between the presence of 1 or more pharmacological discrepancies in each transition and the independent variables (see Statistical Analysis). In this first hypotheses, no variables were significantly associated with discrepancies at the first, second and third transition (Table 4). By contrast, only T4 associations were significant: discharge from a specific local care setting was associated with a discrepancy (p = 0.045), and the length of stay was a statistically significant variable (p<0.001). Gender also appears to be associated with drug discrepancies upon the fourth transition (p = 0.009).

Finally, Table 5 shows the odds ratios at care transitions. Considering the first hypothesis, the following three variables were associated with a higher risk of discrepancies for the patients: long-stay care setting discharge [OR 2.59 (1.042–6.464)], and length of stay of or less than 10 days [OR 11.43 (4.263–30.639)] and between 11 and 30 days [OR 3.72 (1.532–9.037)] at T4.

Second hypothesis: “lack of documentation” was considered a discrepancy

Considering the second hypothesis, the total number of discrepancies was 218; 114 were from a lack of documentation. The pharmacological discrepancies involved 160 patients, so 43.72% had at least one discrepancy. Table 4 shows that no variables were significantly associated with discrepancies at T1 and T2 (Table 4). By contrast, the T3 and T4 associations were significant.

The hospital ward discharge and the local care setting admission in the third transition were associated with discrepancies (p = 0.001 and p<0.001, respectively). Finally, the length of stay and gender were significantly correlated with pharmacological discrepancies in the fourth transition (p = 0.001 and p = 0.049, respectively).

Table 5 shows that integrated home care admission [OR 17 (3.942–76.416)] and patients discharged from orthopedic ward [OR 4 (1.163–14.522)] had higher odds of discrepancy compared to long stay care settings and patients discharged from neurology respectively in T3. Finally, a length of stay of or less than 10 days and between 11 and 30 days was associated with an increased risk of discrepancies [OR 4.69 (2.015–10.932); OR 2.16 (1.119–4.185) respectively] in the fourth transition.

Discussion

To our knowledge this study is the first to consider the complete pathway for patients from hospital admission to local care setting discharge. The literature shows that studies on medication reconciliation have focused on a single point of care transition.

Our study demonstrates that 25.68% and 43.72% of patients had at least one medication discrepancy in the first and second hypotheses, respectively. This rate of medication discrepancy is significant because it concerns the medication discrepancies during the entire care pathway from hospital admission to a local care setting discharge and it is lower than that from most of the reviewed studies, which focused on one transition care, especially in hospital transitions. Indeed, the reported discrepancies, involved 25%-70% of hospital admissions and 33%-96% at hospital discharge [4,6,2022]. Our results may be influenced by the fact that the Italian healthcare system is set up to manage the continuity of care and to reduce fragmentation. The use of units (NOCC; NDCC) that facilitate the transition from hospital care to local care settings along with the presence of structured documentation upon some transitions and the participation of pharmacists, play in favor of low drug discrepancies. This suggestion justifies the best result compared to that reported from other countries with a different healthcare system such as the US [22,27] and Canada [6,20].

Previous studies have shown that the omission of a medication [6] and incomplete prescriptions [20] were the most common discrepancies. These findings are consistent with those of our study, which reported the omission of medication as the most frequent type of discrepancy. Our findings highlight that the variables associated with the risk of discrepancy change from hypothesis 1 to hypothesis 2. Considering “lack of documentation” as missing (hypothesis 1), there is only a statistically significant association at the fourth transition, particularly between the local care setting discharge and the onset of discrepancies. Moreover, our findings show that a long-stay care discharge has an increased risk of drug discrepancy. This result may be explained by the multiple organizations of care settings and their critical issues of work environment [28], by the high number of drugs, and by the frequent lack of standardized procedures for pharmacological reconciliation in this setting. This observation is consistent with those of previous studies that underlined the importance of a drug reconciliation module that keeps track of the changes in therapy for patients who are discharged to long-stay care settings [22,29,30].

Considering the second hypothesis, both hospital ward discharges and local care setting admissions at the third transition are significantly associated with the onset of discrepancies. Our results demonstrate a higher risk of discrepancy upon discharge from the orthopedic ward. This result may be attributed to the observation that this ward did not report a list of drugs in the discharge letter; instead the therapy sheet was included in the clinical chart, which should be sent to the local care setting. However, this practice was not effective because the therapy sheet was frequently not sent during the care transition. Consistent with the findings of Monfort et al., who presented similar results in which the discharge prescriptions from an orthopedic ward were often incomplete or the transmission of medication information at discharge was often lacking [31], we suggest the use of a structured medication reconciliation form. Considering again the second hypothesis, at the third transition, the local care setting admission is associated with medication discrepancies. Admission to an Integrated home care carries the highest risk of discrepancy. This result may be affected by the poorly structured or missing documentation. As reported in the study by Cua et al., documentation of medication changes, their rationale, and whether changes are temporary or permanent are often lacking [32]. A systematic review conducted by Tam et al., stressed that the prevailing pharmacological discrepancy in the home situation is the lack of a drug list [17]. Furthermore, a 2009 review conducted by Bayoumi et al. in primary care demonstrated that the lack of quality information impacts medication reconciliation [33]. The documents in the home situation may have disadvantages of bad handwriting, delays in preparation and missing or incorrect information, increasing the risk of drug discrepancies [34].

A noteworthy result is the risk of drug discrepancy in relation to the length of stay in local care settings, which indicates that there is a higher risk of discrepancy for a ≤10 days length of stay than for longer periods. Comparison of this result with those of other studies are negatively affected by variations in the point of transition. Indeed, few studies in the literature that evaluate the correlation between the length of stay and risk of drug discrepancies focus on the length of stay in a hospital and do not evaluate the local care setting. One study indicates that the possible occurrence of discrepancies is 1.70-fold greater for patients who remain hospitalized for a longer time, but this result is not significant (p = 0.065) [22]. An explanation for a lower risk with a longer stay could be that spending more time in the local settings could improve the understanding of the patient’s condition and result in more attention to his or her therapy.

The lack of communication among professional workers upon care transitions, identified as “lack of documentation”, is one of the issues identified in our study. The importance of structured communication, which could be a part of standardized reconciliation procedures, is a key factor for the continuity of pharmacological therapy [27,35]. There are some strategies to improve the quality of the reconciliation process, which can be extended to any context. First, the study of Whittington et al. detected an increase of 55% in the accuracy of the reconciliation process by introducing a qualified nursing staff and pharmacist staff [36]. Therefore, the process should be performed in an integrated and multidisciplinary manner to support correct admission and discharge medication reconciliation. Moreover, many studies have reported on the pharmacist as a professional reference in the reconciliation process and have encouraged teamwork among medical doctors, nurses and pharmacists [7,3739]. Second, a computerized system could facilitate the electronic collection and transfer of medication information from the time of hospital admission to the local care setting discharge, thus reducing the risk of drug discrepancies [3,4042]. Finally, if the computerization of systems is not possible, a standardized medication reconciliation tool should be introduced to keep track of patient therapy throughout the care transitions [30,43,44].

There are some limitations in our study. First, the reconciliation process is retrospectively assessed. Thus, it was not possible to retrospectively assess whether the drugs involved in the discrepancies were attributable to the fault of the reconnaissance or reconciliation processes; therefore, they were evaluated together. Additionally, it was not possible to measure the potential clinical impact, such as adverse events. Second, the lack of clinical documentation could have limited our analysis because it could not definitively be evaluated whether all cases with a lack of documentation involved medication discrepancies. Third, because of the small sample size, the magnitude of the effect of many of the associations seen cannot be determined with any accuracy. The large size of the confidence intervals seen for major associations indicates that the point estimates for effect size are not likely to be accurate. Finally, the generalizability of our findings could be limited because the results are affected by the type of healthcare system and by the efforts ASLTO2 has made to favor the integration of multiple care settings.

In conclusion, the rate of medication discrepancies across the entire care pathway from hospital admission to a local care setting discharge is low in comparison with results focused on just one care transition. However, this result can be improved, as the local care settings unfortunately have a high lack of communication among professionals with missing documentation, especially in the integrated home care setting. This study suggests that an integrated health care system, such as the Italian system, may influence the prevalence of discrepancies, and highlights the need for structured multidisciplinary and, if possible, computerized medication reconciliation to prevent medication discrepancies and improve the quality of medical documentation.

Acknowledgments

We thank the pharmacist team, Dr. Sara Osella and Dr. Valeria Milone, from San Giovanni Bosco Hospital, ASLTO2, Turin Italy, who have supported the analysis of the pharmacological discrepancies. We also thank Dr. Giorgione, Medical Director of San Giovanni Bosco Hospital, Dr. La Valle, Health Director of ASL TO4, Turin, Italy, Dr. Ratto, local care setting director, ASLTO2, who facilitated data collection and Dr. Laura Belletrutti, San Giovanni Bosco Hospital, ASLTO2, for support in data management.

References

  1. 1. Tjia J, Bonner A, Briesacher BA, McGee S, Terrill E, Miller K. Medication Discrepancies upon Hospital to Skilled Nursing Facility Transitions. J Gen Intern Med. 2009 May;24(5):630–5. pmid:19291332
  2. 2. Redmond P, Carroll H, Grimes T, Galvin R, McDonnell R, Boland F, et al. GPs’ and community pharmacists’ opinions on medication management at transitions of care in Ireland. Fam Pract. 2016 Apr;33(2):172–8. pmid:26984995
  3. 3. Moro Agud M, Menéndez Colino R, Mauleón Ladrero MDC, Ruano Encinar M, Díez Sebastián J, Villamañán Bueno E, et al. Analysis of an electronic medication reconciliation and information at discharge programme for frail elderly patients. Int J Clin Pharm. 2016 Aug;38(4):996–1001. pmid:27306652
  4. 4. Belda-Rustarazo S, Cantero-Hinojosa J, Salmeron-García A, González-García L, Cabeza-Barrera J, Galvez J. Medication reconciliation at admission and discharge: an analysis of prevalence and associated risk factors. Int J Clin Pract. 2015 Nov;69(11):1268–74. pmid:26202091
  5. 5. Hellström LM, Bondesson Å, Höglund P, Eriksson T. Errors in medication history at hospital admission: prevalence and predicting factors. BMC Clin Pharmacol. 2012 Apr 3;12:9. pmid:22471836
  6. 6. Cornish PL, Knowles SR, Marchesano R, Tam V, Shadowitz S, Juurlink DN, et al. Unintended medication discrepancies at the time of hospital admission. Arch Intern Med. 2005 Feb 28;165(4):424–9. pmid:15738372
  7. 7. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-Based Medication Reconciliation Practices: A Systematic Review. Arch Intern Med [Internet]. 2012 Jul 23 [cited 2016 Jan 17];172(14). Available from: http://archinte.jamanetwork.com/article.aspx?doi=10.1001/archinternmed.2012.2246
  8. 8. Forster AJ, Clark HD, Menard A, Dupuis N, Chernish R, Chandok N, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004 Feb 3;170(3):345–9. pmid:14757670
  9. 9. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003 Feb 4;138(3):161–7. pmid:12558354
  10. 10. Moore C, Wisnivesky J, Williams S, McGinn T. Medical errors related to discontinuity of care from an inpatient to an outpatient setting. J Gen Intern Med. 2003 Aug;18(8):646–51. pmid:12911647
  11. 11. Maresca ER, Rossi L, Magnani E, Tassini R, Vitali P, Praticò A. La riconciliazione dei farmaci. Un’esperienza per aumentare la sicurezza nelle fasi di transizione del malato. Boll SIFO. 2012;58(1):17–22.
  12. 12. Action on Patient Safety—High 5s: Assuring Medication Accuracy at Transitions in Care—Medication Reconciliation [Internet]. WHO; 2013 [cited 2017 Mar 8]. http://www.who.int/patientsafety/implementation/solutions/high5s/en/
  13. 13. Hospital: 2017 National Patient Safety Goals [Internet]. [cited 2017 Mar 8]. http://www.jointcommission.org/hap_2017_npsgs/
  14. 14. Pippins JR, Gandhi TK, Hamann C, Ndumele CD, Labonville SA, Diedrichsen EK, et al. Classifying and Predicting Errors of Inpatient Medication Reconciliation. J Gen Intern Med. 2008 Jun 19;23(9):1414–22. pmid:18563493
  15. 15. González-García L, Salmerón-García A, García-Lirola MA, Moya-Roldán S, Belda-Rustarazo S, Cabeza-Barrera J. Medication reconciliation at admission to surgical departments. J Eval Clin Pract. 2015 Jun 17.
  16. 16. Huynh C, Wong ICK, Tomlin S, Halford E, Jani Y, Ghaleb M. An evaluation of paediatric medicines reconciliation at hospital discharge into the community. Int J Pharm Pract. 2016 May;24(3):196–202. pmid:26670624
  17. 17. Tam VC, Knowles SR, Cornish PL, Fine N, Marchesano R, Etchells EE. Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review. CMAJ Can Med Assoc J J Assoc Medicale Can. 2005 Aug 30;173(5):510–5.
  18. 18. Climente-Martí M, García-Mañón ER, Artero-Mora A, Jiménez-Torres NV. Potential risk of medication discrepancies and reconciliation errors at admission and discharge from an inpatient medical service. Ann Pharmacother. 2010 Nov;44(11):1747–54. pmid:20923946
  19. 19. Coleman EA, Smith JD, Raha D, Min S. Posthospital medication discrepancies: prevalence and contributing factors. Arch Intern Med. 2005 Sep 12;165(16):1842–7. pmid:16157827
  20. 20. Wong JD, Bajcar JM, Wong GG, Alibhai SMH, Huh J-H, Cesta A, et al. Medication reconciliation at hospital discharge: evaluating discrepancies. Ann Pharmacother. 2008 Oct;42(10):1373–9. pmid:18780806
  21. 21. Lau HS, Florax C, Porsius AJ, De Boer A. The completeness of medication histories in hospital medical records of patients admitted to general internal medicine wards. Br J Clin Pharmacol. 2000 Jun;49(6):597–603. pmid:10848724
  22. 22. Unroe KT, Pfeiffenberger T, Riegelhaupt S, Jastrzembski J, Lokhnygina Y, Colón-Emeric C. Inpatient Medication Reconciliation at Admission and Discharge: A Retrospective Cohort Study of Age and Other Risk Factors for Medication Discrepancies. Am J Geriatr Pharmacother. 2010 Apr;8(2):115–26. pmid:20439061
  23. 23. Ministero della Salute—D.G. Programmazione sanitaria. Raccomandazione n. 17—Riconciliazione della terapia farmacologica [Internet]. 2015 [cited 2016 Jan 17]. http://www.salute.gov.it/portale/documentazione/p6_2_2_1.jsp?lingua=italiano&id=2354
  24. 24. ASL TO2. Atto Aziedale ASL TO2 [Internet]. 2015 [cited 2017 Oct 13]. http://www.aslto2.piemonte.it/_front/userfiles/atto/Atto_aziendale_19.10.2015.pdf
  25. 25. Regione Piemonte. DGR n°28–7588—“Ridefinizione della rete ospedaliera per acuti e per post-acuti” di cui ai Programmi Operativi 2013–2015 approvati con D.G.R. 25–6992 del 30 dicembre 2013: modificazioni e integrazioni alla D.G.R. 6–5519 del 14 marzo 2013. [Internet]. 2014. http://www.regione.piemonte.it/governo/bollettino/abbonati/2014/24/attach/dgr_07588_830_12052014.pdf
  26. 26. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016.
  27. 27. Stewart AL, Lynch KJ. Medication discrepancies despite pharmacist led medication reconciliation: the challenges of maintaining an accurate medication list in primary care. Pharm Pract [Internet]. 2014 Mar [cited 2017 Feb 11];12(1). Available from: https://www-ncbi-nlm-nih-gov.offcampus.dam.unito.it/pmc/articles/PMC3955863/
  28. 28. Varadarajan RR, Baker KK. An analysis of medication errors in a long term care facility. Res Soc Adm Pharm. 2016 Jul;12(4).
  29. 29. Bruce Bayley K, Savitz LA, Maddalone T, Stoner SE, Hunt JS, Wells R. Evaluation of patient care interventions and recommendations by a transitional care pharmacist. Ther Clin Risk Manag. 2007 Aug;3(4):695–703. pmid:18472993
  30. 30. Blank LJ, Benyo EM, Uriri Glover J. Bridging the Gap in Transitional Care: A Closer Look at Medication Reconciliation. Geriatr Nur (Lond). 2012 Sep;33(5):401–9.
  31. 31. Monfort A-S, Curatolo N, Begue T, Rieutord A, Roy S. Medication at discharge in an orthopaedic surgical ward: quality of information transmission and implementation of a medication reconciliation form. Int J Clin Pharm. 2016 Aug 1;38(4):838–47. pmid:27039090
  32. 32. Cua YM, Kripalani S. Medication Use in the Transition from Hospital to Home. Ann Acad Med Singapore. 2008 Feb;37(2):136. pmid:18327350
  33. 33. Bayoumi I, Howard M, Holbrook AM, Schabort I. Interventions to Improve Medication Reconciliation in Primary Care. Ann Pharmacother. 2009 Oct 1;43(10):1667–75. pmid:19737997
  34. 34. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007 Feb 28;297(8):831–41. pmid:17327525
  35. 35. van Sluisveld N, Zegers M, Natsch S, Wollersheim H. Medication reconciliation at hospital admission and discharge: insufficient knowledge, unclear task reallocation and lack of collaboration as major barriers to medication safety. BMC Health Serv Res. 2012 Jun 21;12(1):170.
  36. 36. Whittington J, Cohen H. OSF healthcare’s journey in patient safety. Qual Manag Health Care. 2004 Mar;13(1):53–9. pmid:14976907
  37. 37. Pherson EC, Shermock KM, Efird LE, Gilmore VT, Nesbit T, LeBlanc Y, et al. Development and implementation of a postdischarge home-based medication management service. Am J Health Syst Pharm. 2014 Sep 15;71(18):1576–83. pmid:25174018
  38. 38. Delate T, Chester EA, Stubbings TW, Barnes CA. Clinical Outcomes of a Home-Based Medication Reconciliation Program After Discharge from a Skilled Nursing Facility. Pharmacother J Hum Pharmacol Drug Ther. 2008 Apr 1;28(4):444–52.
  39. 39. Kwan JL, Lo L, Sampson M, Shojania KG. Medication reconciliation during transitions of care as a patient safety strategy: a systematic review. Ann Intern Med. 2013 Mar 5;158(5 Pt 2):397–403. pmid:23460096
  40. 40. Kramer JS, Hopkins PJ, Rosendale JC, Garrelts JC, Hale LS, Nester TM, et al. Implementation of an electronic system for medication reconciliation. Am J Health Syst Pharm. 2007 Feb 15;64(4):404–22. pmid:17299180
  41. 41. Monte AA, Anderson P, Hoppe JA, Weinshilboum RM, Vasiliou V, Heard KJ. Accuracy of Electronic Medical Record Medication Reconciliation in Emergency Department Patients. J Emerg Med. 2015 Jul;49(1):78–84. pmid:25797942
  42. 42. Nemeth LS, Wessell AM. Improving Medication Safety in Primary Care Using Electronic Health Records. J Patient Saf. 2010 Dec;6(4):238–43. pmid:21500611
  43. 43. Laugaland K, Aase K, Barach P. Interventions to improve patient safety in transitional care—a review of the evidence. Work. 2012 Feb 2;41:2915–24. pmid:22317162
  44. 44. Smith JD, Coleman EA, Min S-J. A new tool for identifying discrepancies in postacute medications for community-dwelling older adults. Am J Geriatr Pharmacother. 2004 Jun;2(2):141–7. pmid:15555490